# packages ----
library(tidycensus)
library(tigris)
library(tidyverse)
library(sf)
library(janitor)
library(here)
library(units)
library(areal)
library(Polychrome)
library(knitr)
library(kableExtra)
library(tmap)
library(patchwork)
library(scales)
library(digest)
library(mapview)
# conflicts ----
library(conflicted)
conflicts_prefer(dplyr::filter)
# options ----
options(scipen = 999) # turn off scientific notation
options(tigris_use_cache = TRUE) # use data caching for tigris
# reference system ----
crs_projected <- 3310 # set a common projected coordinate reference system to be used throughout this analysis - see: https://epsg.io/3310Estimating Demographics and Other Characteristics for Custom Spatial Features – Example Workflow
Accessing U.S. Census Bureau Data and Calculating Weighted Averages with Areal and Population Weighted Interpolation
1 Background
Note
For comments, suggestions, corrections, or questions on anything below, contact david.altare@waterboards.ca.gov, or open an issue on github.
Warning
This document is a work in progress, and may change significantly.
This document provides an example of how to use tools available from the R programming language (R Core Team 2023) to estimate characteristics of any given target spatial area(s) (e.g., neighborhoods, project boundaries, water supplier service areas, etc.) based on data from a source dataset containing the characteristic data of interest (e.g., census data, CalEnvrioScreen scores, etc.), especially when the boundaries of the source and target areas overlap but don’t necessarily align with each other. It also provides some brief background on the various types of data available from the U.S Census Bureau, and links to a few places to find more in-depth information.
This particular example estimates demographic characteristics of community water systems in the Sacramento County area (the target dataset). It uses the tidycensus R package (Walker and Herman 2023) to access selected demographic data from the U.S. Census Bureau (the source dataset) for census units whose spatial extent covers those water systems’ service areas, then uses the areal R package (Prener et al. 2019) to estimate some demographic characteristics of each water system based on that census data. It also relies on a number of other R packages, such as the tidyverse collection of packages (Wickham et al. 2019) for general data management and analysis, and the sf package (Pebesma and Bivand 2023) for working with spatial data.
This example is just intended to be a simplified demonstration of a possible workflow. For a real analysis, additional steps and considerations – that may not be covered here – may be needed to deal with data inconsistencies (e.g., missing or incomplete data), required level of precision and acceptable assumptions (e.g. more fine-grained datasets or more sophisticated techniques could be used to estimate/model population distributions), or other project-specific issues that might arise.
2 Setup
The code block below loads required packages for this analysis, and sets some user-defined options and defaults. If they aren’t already installed on your computer, you can install them with the R command install.packages('package-name') (and replace package-name with the name of the package you want to install).
3 Census Data Overview
This section provides some brief background on the various types of data available from the U.S. Census Bureau (a later section - Section 5 - demonstrates how to retrieve data from the U.S. Census Bureau using the tidycensus R package). Most of the information covered here comes from the book Analyzing US Census Data: Methods, Maps, and Models in R, which is a great source of information if you’d like more detail about any of the topics below (Walker 2023b).
Note
If you’re already familiar with Census data and want to skip this overview, go directly to the next section: Section 4
Different census products/surveys contain data on different variables, at different geographic scales, over varying periods of time, and with varying levels of certainty. Therefore, there are a number of judgement calls to make when determining which type of census data to use for an analysis – e.g., which data product to use (Decennial Census or American Community Survey), which geographic scale to use (e.g., Block, Block Group, Tract, etc.), what time frame to use, which variables to assess, etc.
More detailed information about U.S. Census Bureau’s data products and other topics mentioned below is available here.
3.1 Census Unit Geography / Hierarchy
Publicly available datasets from the U.S Census Bureau generally consist of individual survey responses aggregated to defined census units (e.g., census tracts) that cover varying geographic scales. Some of these units are nested and can be neatly aggregated (e.g., each census tract is composed of a collection of block groups, and each block group is composed of a collection of blocks), while other census units are outside this hierarchy (e.g., Zip Code Tabulation Areas don’t coincide with any other census unit). Figure 1 shows the relationship of all of the various census units.
Commonly used census statistical units like tracts and block groups have target population size ranges, and can be adjusted every 10 years (with the decennial census) based on population changes. For example, all ACS 5-year datasets prior to 2020 use the 2010 boundaries for tracts, block groups, and blocks, and all ACS 5-year datasets from 2020 onward (presumably through 2029) use the 2020 boundaries for those units. Census tracts are generally around 4,000 people, with a range from about 1,200 to 8,000, and block groups generally contain 600 to 3,000 people. Blocks are the smallest census units, and are “areas bounded by visible features, such as streets, roads, streams, and railroad tracks, and by nonvisible boundaries, such as selected property lines and city, township, school district, and county limits and short line-of-sight extensions of streets and roads”. For example, a census block may be “a city block bounded on all sides by streets”, while “blocks in suburban and rural areas may be larger, more irregular in shape, and bounded by a variety of features, such as roads, streams, and transmission lines”.
Caution
Census boundaries can change over time. Commonly used statistical units like tracts, block groups, and blocks tend to be revised every 10 years (with the decennial census), so it’s important to use a census boundary dataset that matches the version of the census demographic data you’re retrieving; otherwise, the demographic data may not match geographic areas in your boundary dataset. In some cases, a census unit that exists in a given year of the census data may not exist at all in a different year’s dataset, because census units can be split or merged when boundaries are revised.
For a list of the different geographic units available for each of the different census products/surveys (see Section 3.2) that can be accessed via the tidycensus package, go here.
3.2 Census Datasets / Surveys
The Decennial Census is conducted every 10 years, and is intended to provide a complete count of the US population and assist with political redistricting. As a result, it collects a relatively limited set of basic demographic data, but (should) provide a high degree of precision (i.e., in general it should provide exact counts). It is available for geographic units down to the census block (the smallest census unit available – see Section 3.1). For information about existing and planned future releases of 2020 census data products, go here.
The American Community Survey (ACS) provides a much larger array of demographic information than the Decennial Census, and is updated more frequently. The ACS is based on a sample of the population (rather than a count of the entire population, as in the Decennial Census), so it represents estimated values rather than precise counts; therefore, each data point is available as an estimate (typically labeled with an “E” in census variable codes, which are discussed in Section 3.3 ) along with an associated margin of error (typically labeled with “M” or “MOE” in census variable codes) around its estimated value.
The ACS is available in two formats. The 5-year ACS is a rolling average of 5 years of data (e.g., the 2021 5-year ACS dataset is an average of the ACS data from 2017 through 2021), and is generally available for geographic units down to the census block group (though some 5-year ACS data may only be available at less granular levels). The 1-year ACS provides data for a single year, and is only available for geographies with population greater than 65,000 (e.g., large cities and counties). Therefore, only the 5-year ACS will be useful for any analysis at a relatively fine scale (e.g., anything that requires data at or more detailed than the census tract level, or any analysis that considers smaller counties/cities – by definition, census tracts always contain significantly fewer than 65,000 people).
In addition to the Decennial Census and ACS data, a number of other census data products/surveys are also available. For example, see the censusapi R package (here or here) for access to over 300 census API endpoints. For historical census data, see the discussion here on using NHGIS, IPUMS, and the ipumsr package.
3.3 Census Variables / Codes
Each census product collects data for many different demographic variables, and each variable is generally associated with an identifier code. In order to access census data programmatically, you often need to know the code associated with each variable of interest. When determining which variables to use, you need to consider what census product contains those variables (see Section 3.2) and how they differ in terms of time frame, precision, spatial granularity (see Section 3.1), etc.
The tidycensus package offers a convenient generic way to search for variables across different census products using the load_variables() function, as described here.
The following websites may also be helpful for exploring the various census data products and finding the variable names and codes they contain:
Census Reporter (for ACS data): https://censusreporter.org/ (especially https://censusreporter.org/topics/table-codes/)
Census Bureau’s list of variable codes, e.g.:
2020 Census codes: https://api.census.gov/data/2020/dec/pl/variables.html
2022 ACS 5 year codes: https://api.census.gov/data/2022/acs/acs5/variables.html
Census Bureau’s data interface (for Decennial Census and ACS, and other census datasets): https://data.census.gov/cedsci/
National Historical Geographic Information System (NHGIS) (for ACS data and historical decennial Census data): https://www.nhgis.org/
4 Target Data Boundaries (Water Systems)
In this section, we’ll get the service area boundaries for Community Water Systems within the Sacramento County area. This will serve as the target dataset – i.e., the set of areas which we’ll be estimating the characteristics of – and will also be used to specifying what census data we want to retrieve. We’ll also get a dataset of county boundaries which overlap the water service areas in this study, which can also help with specifying what census data to access and/or with making maps and visualizations.
4.1 Read Water System Data
In this case, we’ll get the water system dataset from a shapefile that’s saved locally, then transform that dataset into a common coordinate reference system for mapping and analysis (which is defined above in the variable crs_projected).
This water system dataset comes from the California Drinking Water System Area Boundaries dataset. For this example, the dataset has been pre-filtered for systems within Sacramento County (by selecting records where the COUNTY field is “SACRAMENTO”) and for Community Water Systems (by selecting records where the STATE_CLAS field is “COMMUNITY”). Some un-needed fields have also been dropped, remaining fields have been re-orderd.
water_systems_sac <- st_read(here('02_data_input',
'water_supplier_boundaries_sac',
'System_Area_Boundary_Layer_Sac.shp')) %>%
st_transform(crs_projected) # transform to common coordinate systemWe can use the glimpse function (below) to take get a sense of what type of information is available in the water system dataset and how it’s structured.
Note that this dataset already includes a POPULATION variable that indicates the population served by each water system. However, for this analysis we’ll be making our own estimate of the population within each system’s service area based on U.S. Census Bureau data and the spatial representation of the system boundaries. I don’t know exactly how the POPULATION variable was derived in this dataset, and it likely will not exactly match the population estimates from this analysis, but may serve as a useful check to make sure our estimates are reasonable.
glimpse(water_systems_sac)Rows: 62
Columns: 12
$ WATER_SY_1 <chr> "HOOD WATER MAINTENCE DIST [SWS]", "MC CLELLAN MHP", "MAGNO…
$ WATER_SYST <chr> "CA3400101", "CA3400179", "CA3400130", "CA3400135", "CA3400…
$ GLOBALID <chr> "{36268DB3-9DB2-4305-A85A-2C3A85F20F34}", "{E3BF3C3E-D516-4…
$ BOUNDARY_T <chr> "Water Service Area", "Water Service Area", "Water Service …
$ OWNER_TYPE <chr> "L", "P", "P", "P", "P", "P", "P", "P", "P", "P", "P", "P",…
$ COUNTY <chr> "SACRAMENTO", "SACRAMENTO", "SACRAMENTO", "SACRAMENTO", "SA…
$ REGULATING <chr> "LPA64 - SACRAMENTO COUNTY", "LPA64 - SACRAMENTO COUNTY", "…
$ FEDERAL_CL <chr> "COMMUNITY", "COMMUNITY", "COMMUNITY", "COMMUNITY", "COMMUN…
$ STATE_CLAS <chr> "COMMUNITY", "COMMUNITY", "COMMUNITY", "COMMUNITY", "COMMUN…
$ SERVICE_CO <dbl> 82, 199, 34, 64, 128, 83, 28, 50, 164, 5684, 14798, 115, 33…
$ POPULATION <dbl> 100, 700, 40, 150, 256, 150, 32, 100, 350, 18005, 44928, 20…
$ geometry <MULTIPOLYGON [m]> MULTIPOLYGON (((-132703 403..., MULTIPOLYGON (…4.1.1 Alternative Data Retrieval Method
Reading in data from a shapefile is shown above because it’s likely one of the more common ways that users will access their target boundary data. However, depending on the dataset, there may be other ways to access the data. For example, the code chunk below demonstrates an alternative – using the arcgislayers package (Parry 2023) – that connects directly to the source dataset (to retrieve the most recent version) and applies the filters needed to reproduce the dataset in the System_Area_Boundary_Layer_Sac.shp file. Note that this code is not actually used; it is just presented here for reference.
# load arcgislayers package (see: https://r.esri.com/arcgislayers/index.html)
install.packages('pak') # only needed if the pak package is not already installed
pak::pkg_install("R-ArcGIS/arcgislayers", dependencies = TRUE)
library(arcgislayers)
# define link to data source
url_feature <- 'https://gispublic.waterboards.ca.gov/portalserver/rest/services/Drinking_Water/California_Drinking_Water_System_Area_Boundaries/FeatureServer/0'
# connect to data source
water_systems_feature_layer <- arc_open(url_feature)
# download and filter data from source
water_systems_sac <- arc_select(
water_systems_feature_layer,
# apply filters
where = "COUNTY = 'SACRAMENTO' AND STATE_CLASSIFICATION = 'COMMUNITY'",
# select fields
fields = c('WATER_SYSTEM_NAME', 'WATER_SYSTEM_NUMBER', 'GLOBALID',
'BOUNDARY_TYPE', 'OWNER_TYPE_CODE', 'COUNTY',
'REGULATING_AGENCY', 'FEDERAL_CLASSIFICATION', 'STATE_CLASSIFICATION',
'SERVICE_CONNECTIONS', 'POPULATION')) %>%
# transform to common coordinate system
st_transform(crs_projected) %>%
# rename fields to match names from the shapefile (which automatically truncates field names)
rename(WATER_SY_1 = WATER_SYSTEM_NAME,
WATER_SYST = WATER_SYSTEM_NUMBER,
BOUNDARY_T = BOUNDARY_TYPE,
OWNER_TYPE = OWNER_TYPE_CODE,
REGULATING = REGULATING_AGENCY,
FEDERAL_CL = FEDERAL_CLASSIFICATION,
STATE_CLAS = STATE_CLASSIFICATION,
SERVICE_CO = SERVICE_CONNECTIONS)4.2 Get County Boundaries
When accessing census data, it may be useful to know which counties overlap the target dataset, as described below in Section 5 (note that, even though the dataset is filtered for systems in Sacramento county, there are some systems whose boundaries extend into neighboring counties). County boundaries may also be useful for making maps in later stages of the analysis. We can get a dataset of county boundaries in California from the TIGER dataset, which can be accessed with R using the tigris R package (Walker 2023a).
counties_ca <- counties(state = 'CA',
cb = TRUE) %>% # simplified
st_transform(crs_projected) # transform to common coordinate systemThen, we can get a list of counties that overlap with the boundaries of the Sacramento area community water systems obtained above.
counties_overlap <- counties_ca %>% st_filter(water_systems_sac,
.predicate = st_overlaps)
counties_list <- counties_overlap %>% pull(NAME)The counties in the counties_list variable are: San Joaquin, Yolo, Placer, Sacramento.
Figure 2 shows the water systems and county boundaries in an interactive map.
# sac_county <- counties_ca %>% filter(NAME == 'Sacramento')
mapview(counties_overlap, alpha.regions = 0, zcol = 'NAME', layer.name = 'County', legend = FALSE) +
mapview(water_systems_sac, zcol = 'WATER_SY_1', layer.name = 'Water System', legend = FALSE)5 Accessing Census Data
The following sections demonstrate how to retrieve census data from the Decennial Census and the ACS using the tidycensus R package.
In order to use the tidycensus R package, you’ll need to obtain a personal API key from the US Census Bureau (which is free and available to anyone) by signing up here: http://api.census.gov/data/key_signup.html. Once you have your API key, you’ll need to register it in R by entering the command census_api_key(key = "YOUR API KEY", install = TRUE) in the console. Note that the install = TRUE argument means that the key is saved for all future R sessions, so you’ll only need to run that command once on your computer (rather than including it in your scripts). Alternatively, you could save your key to an environment variable and retrieve it using Sys.getenv(). Either way will help you avoid the possibility of entering your API key into any scripts that could be shared publicly.
Caution
Because the boundaries of census units (e.g., tracts, block groups, blocks, etc) can change over time, it’s important to make sure that the version (year) of the census data you’re retrieving matches the version of the census boundary dataset you’re using. The methods shown below retrieve the census boundary dataset together with the census demographic data, which ensures that this won’t be a potential problem. However, if you use a different workflow that retrieves the geographic boundaries and demographic data via separate processes, you should ensure that the versions are consistent.
5.1 Decennial Census
This section retrieves census data from the 2020 U.S. Decennial Census, using the get_decennial function from the tidycensus package. For this example we’re getting data at the ‘Block Group’ level (with the geography = 'block group' argument), for the counties defined above in the counties_list variable (with the county = counties_list argument). By setting the geometry = TRUE argument we’ll be able to get the spatial data – i.e., the boundaries of the census block groups – along with the tabular data defined in the variables argument (see Section 3.3 for information about how to find variable codes). In this case, we’re first saving the census variables we want in the census_vars_decennial object, and also providing descriptive names associated with each variable code, which makes the data easier to work with later. Also, while by default the tidycensus package returns data in long/tidy format, we’re getting the data in wide format for this example (by specifying output = 'wide') because it’ll be easier to work with for the interpolation method described below to estimate demographics for non-census geographies.
Note that, to the extent possible, using census data at the most granular spatial level should provide the best results when estimating demographics for areas whose boundaries don’t align with census unit boundaries. However, depending on the use case, it may require too much time and computational resources to use the most granular spatial data, and may not be necessary to obtain a reasonable estimate. As noted above, in general the most granular level of Decennial Census data available is at the ‘Block’ level (though Decennial Census data may not be available for all data types at the block level, due to data privacy issues), but this example gets data at the ‘Block Group’ level for speed and simplicity. Data at the ‘Block’ level may be a better choice for a real-world analysis if possible.
Also, as a side note, tidycensus returns the Census Bureau’s cartographic boundary shapefiles by default (as opposed to the core TIGER/Line shapefiles). Those default cartographic boundary shapefiles are pre-clipped to the US coastline, and are smaller/faster to process (alternatively you can use cb = FALSE to get the TIGER/Line data) (see here). So the default spatial data returned by tidycensus may be somewhat different than the default spatial data returned by the tigris package, but in general I find it’s best to use the default tidycensus spatial data.
# define variables to pull from the decennial census
census_vars_decennial <- c(
'population_hispanic_or_latino' = 'P2_002N', # Total Hispanic or Latino
'population_white' = 'P2_005N', # White (Not Hispanic or Latino)
'population_black_or_african_american' = 'P2_006N', # Black or African American (Not Hispanic or Latino)
'population_native_american_or_alaska_native' = 'P2_007N', # American Indian and Alaska Native (Not Hispanic or Latino)
'population_asian' = 'P2_008N', # Asian (Not Hispanic or Latino)
'population_pacific_islander' = 'P2_009N', # Native Hawaiian and Other Pacific Islander (Not Hispanic or Latino)
'population_other' = 'P2_010N', # Some other race (Not Hispanic or Latino)
'population_multiple' = 'P2_011N', # Two or more races (Not Hispanic or Latino)
'population_total' = 'P2_001N'
)
# get census data
census_data_decennial <- get_decennial(geography = 'block group', # can be 'block', 'block group', 'tract', 'county', etc.
state = 'CA',
county = counties_list,
year = 2020,
variables = census_vars_decennial,
output = 'wide', # can be 'wide' or 'tidy'
geometry = TRUE,
cache_table = TRUE) The output is an sf object (i.e., a dataframe-like object that also includes spatial data), in wide format, where each row represents a census unit, and the population of each racial/ethnic group is reported in a separate column. Here’s a view of the contents and structure of the Decennial Census data that’s returned:
glimpse(census_data_decennial)Rows: 1,915
Columns: 12
$ GEOID <chr> "060770010004", "060770031…
$ NAME <chr> "Block Group 4, Census Tra…
$ population_hispanic_or_latino <dbl> 292, 588, 603, 851, 158, 1…
$ population_white <dbl> 466, 603, 212, 1178, 801, …
$ population_black_or_african_american <dbl> 19, 155, 26, 137, 7, 11, 5…
$ population_native_american_or_alaska_native <dbl> 2, 3, 5, 17, 1, 4, 16, 9, …
$ population_asian <dbl> 48, 150, 83, 426, 40, 75, …
$ population_pacific_islander <dbl> 1, 6, 1, 7, 0, 5, 4, 41, 2…
$ population_other <dbl> 7, 6, 0, 27, 1, 8, 7, 25, …
$ population_multiple <dbl> 50, 108, 20, 215, 81, 103,…
$ population_total <dbl> 885, 1619, 950, 2858, 1089…
$ geometry <MULTIPOLYGON [°]> MULTIPOLYGON …5.2 American Community Survey (ACS)
To get data from the ACS, you can use the get_acs() function, which is very similar to the get_decennial() function used above.
Note that for this dataset we’ve also included statements to get additional socio-economic data, such as counts of population below and above the poverty line, median household income, and income profile data (income by bracket). Similar to above, we’re first saving the census variables we want in the census_vars_acs object, and also providing descriptive names associated with each variable code to make the data easier to work with later.
# set year
acs_year <- 2022
# define variables to pull from the ACS
census_vars_acs <- c(
# --- population variables ---
'population_total' = 'B01003_001',
'population_hispanic_or_latino' = 'B03002_012', # Total Hispanic or Latino
'population_white' = 'B03002_003', # White (Not Hispanic or Latino)
'population_black_or_african_american' = 'B03002_004', # Black or African American (Not Hispanic or Latino)
'population_native_american_or_alaska_native' = 'B03002_005', # American Indian and Alaska Native (Not Hispanic or Latino)
'population_asian' = 'B03002_006', # Asian (Not Hispanic or Latino)
'population_pacific_islander' = 'B03002_007', # Native Hawaiian and Other Pacific Islander (Not Hispanic or Latino)
'population_other' = 'B03002_008', # Some other race (Not Hispanic or Latino)
'population_multiple' = 'B03002_009', # Two or more races (Not Hispanic or Latino)
# --- poverty variables ---
'poverty_total_assessed' = 'B17021_001', # also available from 'B17020_001' (at the tract level only). Total population for whom poverty status is determined. Poverty status was determined for all people except institutionalized people, people in military group quarters, people in college dormitories, and unrelated individuals under 15 years old. These groups were excluded from the numerator and denominator when calculating poverty rates.
'poverty_below_count' = 'B17021_002', # also available from 'B17020_002' (at the tract level only). Population whose income in the past 12 months is below federal poverty level. A family and every individual in it are considered to be in poverty if the family's total income is less than the dollar value of a threshold that varies depending upon size of family, number of children, & age of householder (for 1- & 2- person households). Income is the sum of wage/salary income; net self-employment income; interest/dividends/net rental/royalty income/income from estates & trusts; Social Security/Railroad Retirement income; Supplemental Security Income (SSI); public assistance/welfare payments; retirement/survivor/disability pensions; & all other income.
'poverty_above_count' = 'B17021_019', # also available from 'B17020_010' (at the tract level only). Population whose income in the past 12 months is at or above federal poverty level. A family and every individual in it are considered to be in poverty if the family's total income is less than the dollar value of a threshold that varies depending upon size of family, number of children, & age of householder (for 1- & 2- person households). Income is the sum of wage/salary income; net self-employment income; interest/dividends/net rental/royalty income/income from estates & trusts; Social Security/Railroad Retirement income; Supplemental Security Income (SSI); public assistance/welfare payments; retirement/survivor/disability pensions; & all other income.
# --- household variables ---
'household_count' = 'B19001_001', # also available from variable 'B19053_001'. A household includes all the people who occupy a housing unit - a house, an apartment, a mobile home, a group of rooms, or a single room that is occupied. People not living in households are classified as living in group quarters.
'average_household_size' = 'B25010_001', # A measure obtained by dividing the number of people living in occupied housing units by the total number of occupied housing units. This measure is rounded to the nearest hundredth.
# --- household income variables ---
'median_household_income' = 'B19013_001', # also available from 'B19019_001' (at the tract level only). Income in the past 12 months is the sum of wage or salary income; net self-employment income; interest, dividends, or net rental or royalty income or income from estates and trusts; Social Security or Railroad Retirement income; Supplemental Security Income (SSI); public assistance or welfare payments; retirement, survivor, or disability pensions; and all other income.
'households_income_below_10k' = 'B19001_002', # count of households with income below $10,000
'households_income_10k_15k' = 'B19001_003', # count of households with income $10,000 to $15,000
'households_income_15k_20k' = 'B19001_004',
'households_income_20k_25k' = 'B19001_005',
'households_income_25k_30k' = 'B19001_006',
'households_income_30k_35k' = 'B19001_007',
'households_income_35k_40k' = 'B19001_008',
'households_income_40k_45k' = 'B19001_009',
'households_income_45k_50k' = 'B19001_010',
'households_income_50k_60k' = 'B19001_011',
'households_income_60k_75k' = 'B19001_012',
'households_income_75k_100k' = 'B19001_013',
'households_income_100k_125k' = 'B19001_014',
'households_income_125k_150k' = 'B19001_015',
'households_income_150k_200k' = 'B19001_016',
'households_income_above_200k' = 'B19001_017', # count of households with income above $200,000
# --- housing costs (% of household income) ---
# Housing Costs as a Percentage of Household Income in the past 12 months - NOTE: THIS TABLE IS NEW FOR THE 2022 ACS, AND WON'T BE AVAILABLE FOR PREVIOUS YEARS - Table B25140 shows the count of households paying more than 30% of their income towards housing costs broken out by three tenure categories (owned with a mortgage, owned without a mortgage, and rented). The table also shows the number of households paying more than 50% of their income toward housing costs.
# 'households_count' = 'B25140_001',
'households_mortgage_total' = 'B25140_002',
'households_mortgage_over30pct' = 'B25140_003',
'households_mortgage_over50pct' = 'B25140_004',
'households_no_mortgage_total' = 'B25140_006',
'households_no_mortgage_over30pct' = 'B25140_007',
'households_no_mortgage_over50pct' = 'B25140_008',
'households_rent_total' = 'B25140_010',
'households_rent_over30pct' = 'B25140_011',
'households_rent_over50pct' = 'B25140_012',
# --- other income / economic variables ---
'per_capita_income' = 'B19301_001' # note: per capita income by race (at block group level) available in table B19301I
)
# get census data
census_data_acs <- get_acs(geography = 'block group',
state = 'CA',
county = counties_list,
year = acs_year,
survey = 'acs5',
variables = census_vars_acs,
output = 'wide', # can be 'wide' or 'tidy'
geometry = TRUE,
cache_table = TRUE)The output is an sf object (i.e., a dataframe-like object that also includes spatial data), in wide format, where each row represents a census unit, and the population of each racial/ethnic group, as well as the median household income, is reported in a separate column. Here’s a view of the contents and structure of the 2022 5-year ACS data that’s returned:
glimpse(census_data_acs)Rows: 1,915
Columns: 85
$ GEOID <chr> "061130107041", "06077004…
$ NAME <chr> "Block Group 1; Census Tr…
$ population_totalE <dbl> 2643, 695, 1768, 1881, 75…
$ population_totalM <dbl> 312, 291, 520, 585, 316, …
$ population_hispanic_or_latinoE <dbl> 370, 36, 38, 327, 545, 37…
$ population_hispanic_or_latinoM <dbl> 112, 24, 59, 298, 273, 28…
$ population_whiteE <dbl> 1609, 659, 1627, 1337, 11…
$ population_whiteM <dbl> 247, 297, 521, 475, 120, …
$ population_black_or_african_americanE <dbl> 100, 0, 0, 1, 26, 272, 39…
$ population_black_or_african_americanM <dbl> 62, 13, 13, 3, 42, 251, 2…
$ population_native_american_or_alaska_nativeE <dbl> 2, 0, 41, 0, 0, 0, 0, 0, …
$ population_native_american_or_alaska_nativeM <dbl> 4, 13, 58, 13, 13, 13, 13…
$ population_asianE <dbl> 306, 0, 45, 0, 13, 105, 5…
$ population_asianM <dbl> 107, 13, 71, 13, 21, 116,…
$ population_pacific_islanderE <dbl> 0, 0, 0, 98, 0, 0, 0, 0, …
$ population_pacific_islanderM <dbl> 13, 13, 13, 98, 13, 13, 1…
$ population_otherE <dbl> 5, 0, 0, 0, 0, 39, 0, 0, …
$ population_otherM <dbl> 9, 13, 13, 13, 13, 63, 13…
$ population_multipleE <dbl> 251, 0, 17, 118, 57, 13, …
$ population_multipleM <dbl> 123, 13, 27, 125, 76, 20,…
$ poverty_total_assessedE <dbl> 2523, 695, 1768, 1847, 75…
$ poverty_total_assessedM <dbl> 308, 291, 520, 582, 316, …
$ poverty_below_countE <dbl> 760, 98, 101, 328, 151, 2…
$ poverty_below_countM <dbl> 209, 97, 83, 370, 130, 22…
$ poverty_above_countE <dbl> 1763, 597, 1667, 1519, 60…
$ poverty_above_countM <dbl> 255, 277, 517, 423, 292, …
$ household_countE <dbl> 958, 246, 680, 718, 191, …
$ household_countM <dbl> 89, 98, 167, 162, 67, 151…
$ average_household_sizeE <dbl> 2.56, 2.65, 2.59, 2.62, 3…
$ average_household_sizeM <dbl> 0.20, 0.70, 0.33, 0.40, 1…
$ median_household_incomeE <dbl> 64821, 96250, 123500, 667…
$ median_household_incomeM <dbl> 18076, 90011, 76420, 1532…
$ households_income_below_10kE <dbl> 107, 46, 18, 47, 0, 10, 3…
$ households_income_below_10kM <dbl> 51, 52, 29, 38, 13, 16, 2…
$ households_income_10k_15kE <dbl> 53, 0, 0, 0, 0, 24, 23, 3…
$ households_income_10k_15kM <dbl> 37, 13, 13, 13, 13, 41, 2…
$ households_income_15k_20kE <dbl> 25, 0, 0, 13, 0, 18, 0, 7…
$ households_income_15k_20kM <dbl> 25, 13, 13, 19, 13, 29, 1…
$ households_income_20k_25kE <dbl> 50, 0, 0, 12, 0, 0, 66, 0…
$ households_income_20k_25kM <dbl> 33, 13, 13, 21, 13, 13, 5…
$ households_income_25k_30kE <dbl> 32, 13, 30, 74, 10, 35, 8…
$ households_income_25k_30kM <dbl> 28, 21, 36, 66, 17, 42, 9…
$ households_income_30k_35kE <dbl> 48, 11, 0, 10, 28, 16, 0,…
$ households_income_30k_35kM <dbl> 38, 17, 13, 17, 42, 19, 1…
$ households_income_35k_40kE <dbl> 14, 0, 0, 24, 14, 0, 73, …
$ households_income_35k_40kM <dbl> 16, 13, 13, 23, 17, 13, 6…
$ households_income_40k_45kE <dbl> 17, 0, 19, 17, 0, 0, 36, …
$ households_income_40k_45kM <dbl> 18, 13, 30, 18, 13, 13, 4…
$ households_income_45k_50kE <dbl> 39, 0, 15, 15, 0, 47, 11,…
$ households_income_45k_50kM <dbl> 42, 13, 25, 19, 13, 63, 2…
$ households_income_50k_60kE <dbl> 74, 40, 76, 28, 38, 90, 7…
$ households_income_50k_60kM <dbl> 52, 63, 78, 35, 36, 112, …
$ households_income_60k_75kE <dbl> 46, 9, 126, 156, 14, 61, …
$ households_income_60k_75kM <dbl> 24, 15, 102, 137, 18, 52,…
$ households_income_75k_100kE <dbl> 40, 4, 15, 71, 27, 74, 75…
$ households_income_75k_100kM <dbl> 25, 10, 25, 50, 45, 72, 4…
$ households_income_100k_125kE <dbl> 85, 31, 53, 125, 10, 0, 0…
$ households_income_100k_125kM <dbl> 36, 36, 51, 57, 16, 13, 1…
$ households_income_125k_150kE <dbl> 42, 20, 101, 10, 8, 22, 6…
$ households_income_125k_150kM <dbl> 26, 28, 81, 16, 12, 30, 7…
$ households_income_150k_200kE <dbl> 68, 0, 173, 30, 42, 0, 24…
$ households_income_150k_200kM <dbl> 39, 13, 134, 29, 40, 13, …
$ households_income_above_200kE <dbl> 218, 72, 54, 86, 0, 8, 0,…
$ households_income_above_200kM <dbl> 61, 65, 45, 60, 13, 16, 1…
$ households_mortgage_totalE <dbl> 254, 94, 251, 452, 97, 19…
$ households_mortgage_totalM <dbl> 66, 72, 105, 164, 62, 151…
$ households_mortgage_over30pctE <dbl> 36, 25, 88, 223, 32, 97, …
$ households_mortgage_over30pctM <dbl> 24, 27, 87, 144, 42, 118,…
$ households_mortgage_over50pctE <dbl> 16, 14, 68, 38, 28, 13, 9…
$ households_mortgage_over50pctM <dbl> 13, 23, 80, 38, 42, 20, 1…
$ households_no_mortgage_totalE <dbl> 129, 80, 324, 226, 37, 0,…
$ households_no_mortgage_totalM <dbl> 33, 54, 158, 65, 31, 13, …
$ households_no_mortgage_over30pctE <dbl> 0, 0, 18, 126, 0, 0, 21, …
$ households_no_mortgage_over30pctM <dbl> 13, 13, 29, 75, 13, 13, 2…
$ households_no_mortgage_over50pctE <dbl> 0, 0, 18, 91, 0, 0, 0, 15…
$ households_no_mortgage_over50pctM <dbl> 13, 13, 29, 81, 13, 13, 1…
$ households_rent_totalE <dbl> 575, 72, 105, 40, 57, 215…
$ households_rent_totalM <dbl> 82, 74, 95, 40, 44, 95, 1…
$ households_rent_over30pctE <dbl> 426, 0, 90, 30, 18, 110, …
$ households_rent_over30pctM <dbl> 86, 13, 95, 36, 23, 75, 1…
$ households_rent_over50pctE <dbl> 319, 0, 13, 9, 18, 64, 12…
$ households_rent_over50pctM <dbl> 85, 13, 23, 14, 23, 61, 1…
$ per_capita_incomeE <dbl> 48316, 49981, 45172, 3434…
$ per_capita_incomeM <dbl> 8024, 18092, 7975, 8289, …
$ geometry <MULTIPOLYGON [°]> MULTIPOLYGON…Note that the dataset that’s returned includes fields corresponding to Margin of Error (MOE) for each variable we’ve requested (these are the fields that end with two digits and an M – e.g., “001M”), since, as noted above in Section 3.2 , the ACS is based on a sample of the population and reports estimated values.
Tip
It is possible to calculate MOEs for derived estimates – e.g., when aggregating groups of census units – and in many cases it may be worthwhile to do that to provide extra context to the data. However, it may be difficult to do for more complex aggregations, such as the area and population weighted interpolations shown below. For guidance on how calculate MOEs for some types of derived estimates, see this document.
Because we won’t be incorporating those MOEs into the analysis below, we can drop them for this example, then clean up the field names.
# drop MOE fields
census_data_acs <- census_data_acs %>%
select(-matches('M$')) # the $ specifies "ends with"
# clean names
names(census_data_acs) <- names(census_data_acs) %>%
str_remove('E$') %>% # remove 'E' (estimate) from field names
str_replace('NAM', 'NAME') # add 'E' back to NAME fieldHere’s a view of the contents and structure of the revised 2022 5-year ACS dataset:
glimpse(census_data_acs)Rows: 1,915
Columns: 44
$ GEOID <chr> "061130107041", "060770049…
$ NAME <chr> "Block Group 1; Census Tra…
$ population_total <dbl> 2643, 695, 1768, 1881, 754…
$ population_hispanic_or_latino <dbl> 370, 36, 38, 327, 545, 376…
$ population_white <dbl> 1609, 659, 1627, 1337, 113…
$ population_black_or_african_american <dbl> 100, 0, 0, 1, 26, 272, 394…
$ population_native_american_or_alaska_native <dbl> 2, 0, 41, 0, 0, 0, 0, 0, 2…
$ population_asian <dbl> 306, 0, 45, 0, 13, 105, 50…
$ population_pacific_islander <dbl> 0, 0, 0, 98, 0, 0, 0, 0, 0…
$ population_other <dbl> 5, 0, 0, 0, 0, 39, 0, 0, 0…
$ population_multiple <dbl> 251, 0, 17, 118, 57, 13, 1…
$ poverty_total_assessed <dbl> 2523, 695, 1768, 1847, 754…
$ poverty_below_count <dbl> 760, 98, 101, 328, 151, 27…
$ poverty_above_count <dbl> 1763, 597, 1667, 1519, 603…
$ household_count <dbl> 958, 246, 680, 718, 191, 4…
$ average_household_size <dbl> 2.56, 2.65, 2.59, 2.62, 3.…
$ median_household_income <dbl> 64821, 96250, 123500, 6676…
$ households_income_below_10k <dbl> 107, 46, 18, 47, 0, 10, 30…
$ households_income_10k_15k <dbl> 53, 0, 0, 0, 0, 24, 23, 34…
$ households_income_15k_20k <dbl> 25, 0, 0, 13, 0, 18, 0, 7,…
$ households_income_20k_25k <dbl> 50, 0, 0, 12, 0, 0, 66, 0,…
$ households_income_25k_30k <dbl> 32, 13, 30, 74, 10, 35, 80…
$ households_income_30k_35k <dbl> 48, 11, 0, 10, 28, 16, 0, …
$ households_income_35k_40k <dbl> 14, 0, 0, 24, 14, 0, 73, 0…
$ households_income_40k_45k <dbl> 17, 0, 19, 17, 0, 0, 36, 0…
$ households_income_45k_50k <dbl> 39, 0, 15, 15, 0, 47, 11, …
$ households_income_50k_60k <dbl> 74, 40, 76, 28, 38, 90, 73…
$ households_income_60k_75k <dbl> 46, 9, 126, 156, 14, 61, 3…
$ households_income_75k_100k <dbl> 40, 4, 15, 71, 27, 74, 75,…
$ households_income_100k_125k <dbl> 85, 31, 53, 125, 10, 0, 0,…
$ households_income_125k_150k <dbl> 42, 20, 101, 10, 8, 22, 67…
$ households_income_150k_200k <dbl> 68, 0, 173, 30, 42, 0, 24,…
$ households_income_above_200k <dbl> 218, 72, 54, 86, 0, 8, 0, …
$ households_mortgage_total <dbl> 254, 94, 251, 452, 97, 190…
$ households_mortgage_over30pct <dbl> 36, 25, 88, 223, 32, 97, 4…
$ households_mortgage_over50pct <dbl> 16, 14, 68, 38, 28, 13, 9,…
$ households_no_mortgage_total <dbl> 129, 80, 324, 226, 37, 0, …
$ households_no_mortgage_over30pct <dbl> 0, 0, 18, 126, 0, 0, 21, 2…
$ households_no_mortgage_over50pct <dbl> 0, 0, 18, 91, 0, 0, 0, 15,…
$ households_rent_total <dbl> 575, 72, 105, 40, 57, 215,…
$ households_rent_over30pct <dbl> 426, 0, 90, 30, 18, 110, 1…
$ households_rent_over50pct <dbl> 319, 0, 13, 9, 18, 64, 129…
$ per_capita_income <dbl> 48316, 49981, 45172, 34343…
$ geometry <MULTIPOLYGON [°]> MULTIPOLYGON …For further analysis, we may want to get the statewide data as a baseline for comparison (this could also be done for other scales, like the county level). We can use a similar process to get that data and clean/format it to match the more detailed data obtained above. Note that in this case we’re also using the 5-year ACS (even though the 1-year ACS is also available at the statewide level, and would provide more up-to-date data) so that the statewide data will be directly comparable to the block group level data obtained above.
census_data_acs_state <- get_acs(geography = 'state',
state = 'CA',
year = acs_year,
survey = 'acs5',
variables = census_vars_acs,
output = 'wide', # can be 'wide' or 'tidy'
geometry = TRUE,
cache_table = TRUE) %>%
select(-matches('M$')) %>% # the $ specifies "ends with"
# clean names (note this is a little different than the way we renamed fields above, either works)
rename_with(.fn = ~ str_remove(., # remove 'E' (estimate) from field names
pattern = 'E$')) %>%
rename_with(.fn = ~ str_replace(., # add 'E' back to NAME field
pattern = 'NAM',
replacement = 'NAME'))Here’s a view of the contents and structure of the revised 2022 5-year ACS statewide dataset:
glimpse(census_data_acs_state)Rows: 1
Columns: 44
$ GEOID <chr> "06"
$ NAME <chr> "California"
$ population_total <dbl> 39356104
$ population_hispanic_or_latino <dbl> 15617930
$ population_white <dbl> 13848294
$ population_black_or_african_american <dbl> 2102510
$ population_native_american_or_alaska_native <dbl> 114271
$ population_asian <dbl> 5861649
$ population_pacific_islander <dbl> 135460
$ population_other <dbl> 176652
$ population_multiple <dbl> 1499338
$ poverty_total_assessed <dbl> 38643585
$ poverty_below_count <dbl> 4685272
$ poverty_above_count <dbl> 33958313
$ household_count <dbl> 13315822
$ average_household_size <dbl> 2.89
$ median_household_income <dbl> 91905
$ households_income_below_10k <dbl> 589276
$ households_income_10k_15k <dbl> 427356
$ households_income_15k_20k <dbl> 348598
$ households_income_20k_25k <dbl> 393094
$ households_income_25k_30k <dbl> 405899
$ households_income_30k_35k <dbl> 393612
$ households_income_35k_40k <dbl> 377591
$ households_income_40k_45k <dbl> 386978
$ households_income_45k_50k <dbl> 388905
$ households_income_50k_60k <dbl> 764521
$ households_income_60k_75k <dbl> 1065431
$ households_income_75k_100k <dbl> 1595276
$ households_income_100k_125k <dbl> 1337354
$ households_income_125k_150k <dbl> 1032621
$ households_income_150k_200k <dbl> 1429049
$ households_income_above_200k <dbl> 2380261
$ households_mortgage_total <dbl> 5067173
$ households_mortgage_over30pct <dbl> 1796037
$ households_mortgage_over50pct <dbl> 775704
$ households_no_mortgage_total <dbl> 2340188
$ households_no_mortgage_over30pct <dbl> 352984
$ households_no_mortgage_over50pct <dbl> 180034
$ households_rent_total <dbl> 5908461
$ households_rent_over30pct <dbl> 2933613
$ households_rent_over50pct <dbl> 1528715
$ per_capita_income <dbl> 45591
$ geometry <MULTIPOLYGON [°]> MULTIPOLYGON (((-118.6044 …5.3 Plot Results
Figure 3 shows the datasets that we’ll use below to compute water system demographics (zoomed in to the area around the water systems in this study). Each water system – the target dataset – is shown with a different (randomly chosen) color. The boundaries of the census data – the source dataset – are shown in red; in this case we’ll use the 2022 5-year ACS dataset. County boundaries are shown in grey (Sacramento county is show with a bold grey line).
# get the map boundaries, using the boundaries of the water suppliers and the
# Sacramento county boundary
bounds_map <- counties_ca %>%
filter(NAME == 'Sacramento') %>%
st_union(water_systems_sac) %>%
st_buffer(100) %>%
st_bbox()
# make a color palette with a distinct (random) color for supplier
set.seed(12345)
pal <- createPalette(nrow(water_systems_sac),
c("#FF0000", "#00FF00", "#0000FF"),
range = c(30, 80))
# make the plot
plot_census <- ggplot() +
geom_sf(data = water_systems_sac,
aes(fill = WATER_SYST, alpha = 0.8),
# fill = alpha('blue', 0.8),
color = 'black',
linewidth = 0.1,
show.legend = FALSE) +
scale_fill_manual(values = unname(pal)) +
geom_sf(data = census_data_acs %>%
st_transform(crs_projected),
fill = NA,
color = alpha('red', 1),
linewidth = 0.1) +
geom_sf(data = counties_ca %>%
filter(NAME %in% counties_list),
fill = NA,
color = 'grey',
linewidth = 0.5) +
geom_sf(data = counties_ca %>%
filter(NAME == 'Sacramento'),
fill = NA,
color = 'darkgrey',
linewidth = 1) + # bold line for Sacramento County
coord_sf(xlim = c(bounds_map$xmin, bounds_map$xmax),
ylim = c(bounds_map$ymin, bounds_map$ymax)) +
theme_void()
# show the plot
plot_census
6 Compute Water System Demographics
Now we can perform the calculations to estimate demographic characteristics for our target areas (water system service boundaries in the Sacramento County area) from our source demographic dataset (the census data we obtained above). For this example, we’ll use the 2022 5-year ACS data that we retrieved above (which is saved in the census_data_acs variable) as our source of demographic data, and we’ll estimate the following for each water system’s service area:
- Population of each racial/ethnic group (using the racial/ethnic categories defined in the census dataset), and each racial/ethnic group’s portion of the total service area population
- Socio-economic variables like poverty rate, median household income, income distributions, and per capita income
There are multiple ways this estimation can be done, but one of the most simple and straightforward is using an areal interpolation (essentially an area weighted average). The major simplifying assumption of this approach is that the population or other characteristic(s) of interest are evenly distributed within each unit in the source data – e.g., in this case we’re assuming that population (including the total population and the population of each racial/ethic group) and household income is evenly distributed within each census block group.
6.1 Prepare Census Data
To calculate demographics for the target areas, we need to prepare the census data. First, we need to transform the 2022 5-year ACS dataset into the common projected coordinate reference system defined above.
# transform to common coordinate reference system
census_data_acs <- census_data_acs %>%
st_transform(crs_projected)We can then apply some transformations to the 2022 5-year ACS dataset prior to performing the areal interpolation to simplify the results and speed the computation.
This isn’t strictly necessary, but we can simplify the census dataset by filtering for census units that overlap with one of the water systems in our dataset.
census_data_filter <- census_data_acs %>%
st_filter(water_systems_sac,
.predicate = st_intersects)We can also drop census units with zero population, since they won’t contribute anything to our calculations.
## drop census units with zero population
census_data_filter <- census_data_filter %>%
filter(population_total > 0)You may also want to transform the census data in other ways. For example, we can combine the ‘other’ and ‘multiple’ racial/ethnic groupings into one ‘other or multiple’ racial/ethnic group.
## combine other and multiple
census_data_filter <- census_data_filter %>%
mutate('population_other_or_multiple' = population_other + population_multiple,
.after = population_pacific_islander) %>%
select(-c(population_other, population_multiple))Here’s a view of the contents and structure of the transformed census dataset:
glimpse(census_data_filter)Rows: 1,049
Columns: 43
$ GEOID <chr> "060670081451", "060670082…
$ NAME <chr> "Block Group 1; Census Tra…
$ population_total <dbl> 1768, 1881, 1098, 2748, 17…
$ population_hispanic_or_latino <dbl> 38, 327, 376, 782, 366, 17…
$ population_white <dbl> 1627, 1337, 293, 1817, 840…
$ population_black_or_african_american <dbl> 0, 1, 272, 26, 351, 111, 1…
$ population_native_american_or_alaska_native <dbl> 41, 0, 0, 26, 0, 0, 0, 31,…
$ population_asian <dbl> 45, 0, 105, 58, 144, 61, 0…
$ population_pacific_islander <dbl> 0, 98, 0, 0, 27, 13, 0, 0,…
$ population_other_or_multiple <dbl> 17, 118, 52, 39, 15, 37, 1…
$ poverty_total_assessed <dbl> 1768, 1847, 1098, 2748, 17…
$ poverty_below_count <dbl> 101, 328, 272, 116, 127, 3…
$ poverty_above_count <dbl> 1667, 1519, 826, 2632, 161…
$ household_count <dbl> 680, 718, 405, 905, 732, 6…
$ average_household_size <dbl> 2.59, 2.62, 2.71, 2.97, 2.…
$ median_household_income <dbl> 123500, 66768, 56216, 1172…
$ households_income_below_10k <dbl> 18, 47, 10, 22, 6, 107, 10…
$ households_income_10k_15k <dbl> 0, 0, 24, 0, 15, 231, 10, …
$ households_income_15k_20k <dbl> 0, 13, 18, 0, 51, 12, 12, …
$ households_income_20k_25k <dbl> 0, 12, 0, 13, 10, 12, 0, 6…
$ households_income_25k_30k <dbl> 30, 74, 35, 41, 0, 9, 0, 3…
$ households_income_30k_35k <dbl> 0, 10, 16, 0, 102, 47, 15,…
$ households_income_35k_40k <dbl> 0, 24, 0, 0, 29, 6, 0, 0, …
$ households_income_40k_45k <dbl> 19, 17, 0, 13, 21, 8, 0, 0…
$ households_income_45k_50k <dbl> 15, 15, 47, 56, 91, 0, 0, …
$ households_income_50k_60k <dbl> 76, 28, 90, 53, 70, 17, 18…
$ households_income_60k_75k <dbl> 126, 156, 61, 140, 67, 20,…
$ households_income_75k_100k <dbl> 15, 71, 74, 40, 74, 27, 11…
$ households_income_100k_125k <dbl> 53, 125, 0, 77, 89, 52, 13…
$ households_income_125k_150k <dbl> 101, 10, 22, 171, 89, 0, 1…
$ households_income_150k_200k <dbl> 173, 30, 0, 180, 0, 0, 150…
$ households_income_above_200k <dbl> 54, 86, 8, 99, 18, 116, 18…
$ households_mortgage_total <dbl> 251, 452, 190, 641, 248, 1…
$ households_mortgage_over30pct <dbl> 88, 223, 97, 195, 87, 0, 3…
$ households_mortgage_over50pct <dbl> 68, 38, 13, 76, 18, 0, 33,…
$ households_no_mortgage_total <dbl> 324, 226, 0, 110, 98, 0, 6…
$ households_no_mortgage_over30pct <dbl> 18, 126, 0, 0, 37, 0, 32, …
$ households_no_mortgage_over50pct <dbl> 18, 91, 0, 0, 21, 0, 10, 0…
$ households_rent_total <dbl> 105, 40, 215, 154, 386, 65…
$ households_rent_over30pct <dbl> 90, 30, 110, 0, 268, 345, …
$ households_rent_over50pct <dbl> 13, 9, 64, 0, 141, 198, 0,…
$ per_capita_income <dbl> 45172, 34343, 24410, 39796…
$ geometry <MULTIPOLYGON [m]> MULTIPOLYGON …6.2 Areal Interpolation
Caution
Areal interpolation, based on area weighted averaging, is possibly most useful for estimating count data for overlapping polygons - like population size or number of households - since we generally have no other information about how population density varies within the source polygons (unless significantly more effort is invested, such as looking at aerial imagery data).
However, for intensive variables that are associated with the estimated count data - per capita or median household income - it may be be more appropriate to use the corresponding estimated count data (rather than area) as a weighting factor. For example, for per capita income, we can use the estimated population counts to produce a population weighted average per capia income (rather than an area weighted average per capita income).
Tip
In addition, there may be cases where we can use more granular block-level population data from the decennial census to estimate population densities and distributions within larger census units, like block groups and tracts.
See Section 7.1 for more information.
There are a couple of ways to implement the areal interpolation method. The example below uses the aw_interpolate function from the areal R package. The sf package’s st_interpolate_aw function provides similar functionality. It’s also possible to ‘manually’ implement the process using lower level functions from the sf package, which can be useful for even more control, but is more complicated to implement (see Section 6.6 below for examples of ‘manual’ calculations that demonstrate how the interpolation function works and provide a check on the results).
Note that there are some settings that you may need to modify in the aw_interpolate function depending on the type of analysis you’re doing. In particular, for more information about extensive versus intensive interpolations, see this section of the documenation. For more information about the weight argument – which can be either sum or total – see this section of the documentation.
Caution
Using interpolated results for the variables in the intensive list below may not be the best approach. For variables like average houshold size or per capita income, it may be more appropriate to use a separate process that first calculates relevant counts in each overlapping census unit (e.g., number of households or number of people, respectively), then weight the results using those counts (see section [TO BE ADDED] below). For median household income, it may be more appropriate to use the counts in each income bracket to estimate a median income and/or present the income distribution rather than a single value.
water_systems_sac_demographics <- water_systems_sac %>%
aw_interpolate(tid = WATER_SY_1,
source = census_data_filter,
sid = GEOID,
weight = 'total',
output = 'sf',
extensive = c(
'population_total',
'population_hispanic_or_latino',
'population_white',
'population_black_or_african_american',
'population_native_american_or_alaska_native',
'population_asian',
'population_pacific_islander',
'population_other_or_multiple',
'poverty_total_assessed',
'poverty_below_count',
'poverty_above_count',
'household_count',
'households_income_below_10k',
'households_income_10k_15k',
'households_income_15k_20k',
'households_income_20k_25k',
'households_income_25k_30k',
'households_income_30k_35k',
'households_income_35k_40k',
'households_income_40k_45k',
'households_income_45k_50k',
'households_income_50k_60k',
'households_income_60k_75k',
'households_income_75k_100k',
'households_income_100k_125k',
'households_income_125k_150k',
'households_income_150k_200k',
'households_income_above_200k',
'households_mortgage_total',
'households_mortgage_over30pct',
'households_mortgage_over50pct',
'households_no_mortgage_total',
'households_no_mortgage_over30pct',
'households_no_mortgage_over50pct',
'households_rent_total',
'households_rent_over30pct',
'households_rent_over50pct'
)#,
# intensive = c(
# 'median_household_income',
# 'average_household_size',
# 'per_capita_income'
# )
)We’ve now got a dataset with the selected census data estimated for each of the target geographic features (water system service areas). Here’s a view of the contents and structure of the dataset:
glimpse(water_systems_sac_demographics)Rows: 62
Columns: 49
$ WATER_SY_1 <chr> "HOOD WATER MAINTENCE DIST…
$ WATER_SYST <chr> "CA3400101", "CA3400179", …
$ GLOBALID <chr> "{36268DB3-9DB2-4305-A85A-…
$ BOUNDARY_T <chr> "Water Service Area", "Wat…
$ OWNER_TYPE <chr> "L", "P", "P", "P", "P", "…
$ COUNTY <chr> "SACRAMENTO", "SACRAMENTO"…
$ REGULATING <chr> "LPA64 - SACRAMENTO COUNTY…
$ FEDERAL_CL <chr> "COMMUNITY", "COMMUNITY", …
$ STATE_CLAS <chr> "COMMUNITY", "COMMUNITY", …
$ SERVICE_CO <dbl> 82, 199, 34, 64, 128, 83, …
$ POPULATION <dbl> 100, 700, 40, 150, 256, 15…
$ household_count <dbl> 0.41121610, 82.16845104, 0…
$ households_income_100k_125k <dbl> 0.052575575, 3.423685460, …
$ households_income_10k_15k <dbl> 0.000000000, 1.943172829, …
$ households_income_125k_150k <dbl> 0.00000000, 0.00000000, 0.…
$ households_income_150k_200k <dbl> 0.000000000, 2.961025263, …
$ households_income_15k_20k <dbl> 0.000000000, 3.423685460, …
$ households_income_20k_25k <dbl> 0.000000000, 6.754838880, …
$ households_income_25k_30k <dbl> 0.0000000000, 11.381440853…
$ households_income_30k_35k <dbl> 0.0000000000, 1.9431728286…
$ households_income_35k_40k <dbl> 0.0694748669, 1.5730446707…
$ households_income_40k_45k <dbl> 0.037553982, 1.387980592, …
$ households_income_45k_50k <dbl> 0.018776991, 3.146089341, …
$ households_income_50k_60k <dbl> 0.046942478, 1.295448552, …
$ households_income_60k_75k <dbl> 0.050697876, 15.452850589,…
$ households_income_75k_100k <dbl> 0.065719469, 19.709324404,…
$ households_income_above_200k <dbl> 0.013143894, 0.000000000, …
$ households_income_below_10k <dbl> 0.056330973, 7.772691314, …
$ households_mortgage_over30pct <dbl> 0.054453274, 3.793813618, …
$ households_mortgage_over50pct <dbl> 0.000000000, 1.573044671, …
$ households_mortgage_total <dbl> 0.054453274, 9.345735985, …
$ households_no_mortgage_over30pct <dbl> 0.056330973, 0.832788355, …
$ households_no_mortgage_over50pct <dbl> 0.0563309732, 0.8327883551…
$ households_no_mortgage_total <dbl> 0.16523752, 24.52099046, 0…
$ households_rent_over30pct <dbl> 0.076985663, 33.866726441,…
$ households_rent_over50pct <dbl> 0.0488201768, 26.649227363…
$ households_rent_total <dbl> 0.191525309, 48.301724596,…
$ population_asian <dbl> 0.000000000, 42.842334268,…
$ population_black_or_african_american <dbl> 0.000000000, 64.679895580,…
$ population_hispanic_or_latino <dbl> 0.805532917, 51.910474135,…
$ population_native_american_or_alaska_native <dbl> 0.0000000, 0.0000000, 0.00…
$ population_other_or_multiple <dbl> 0.024410088, 1.665576710, …
$ population_pacific_islander <dbl> 0.0000000, 0.0000000, 0.00…
$ population_total <dbl> 1.16417345, 269.36076686, …
$ population_white <dbl> 0.33423044, 108.26248616, …
$ poverty_above_count <dbl> 0.96138194, 168.40831181, …
$ poverty_below_count <dbl> 0.20279150, 100.95245505, …
$ poverty_total_assessed <dbl> 1.16417345, 269.36076686, …
$ geometry <MULTIPOLYGON [m]> MULTIPOLYGON …Table 1 provides a complete view of the dataset:
water_systems_sac_demographics %>%
kable(caption = 'A Caption') %>%
scroll_box(height = "400px")| WATER_SY_1 | WATER_SYST | GLOBALID | BOUNDARY_T | OWNER_TYPE | COUNTY | REGULATING | FEDERAL_CL | STATE_CLAS | SERVICE_CO | POPULATION | household_count | households_income_100k_125k | households_income_10k_15k | households_income_125k_150k | households_income_150k_200k | households_income_15k_20k | households_income_20k_25k | households_income_25k_30k | households_income_30k_35k | households_income_35k_40k | households_income_40k_45k | households_income_45k_50k | households_income_50k_60k | households_income_60k_75k | households_income_75k_100k | households_income_above_200k | households_income_below_10k | households_mortgage_over30pct | households_mortgage_over50pct | households_mortgage_total | households_no_mortgage_over30pct | households_no_mortgage_over50pct | households_no_mortgage_total | households_rent_over30pct | households_rent_over50pct | households_rent_total | population_asian | population_black_or_african_american | population_hispanic_or_latino | population_native_american_or_alaska_native | population_other_or_multiple | population_pacific_islander | population_total | population_white | poverty_above_count | poverty_below_count | poverty_total_assessed | geometry |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| HOOD WATER MAINTENCE DIST [SWS] | CA3400101 | {36268DB3-9DB2-4305-A85A-2C3A85F20F34} | Water Service Area | L | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 82 | 100 | 0.4112161 | 0.0525756 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0694749 | 0.0375540 | 0.0187770 | 0.0469425 | 0.0506979 | 0.0657195 | 0.0131439 | 0.0563310 | 0.0544533 | 0.0000000 | 0.0544533 | 0.0563310 | 0.0563310 | 0.1652375 | 0.0769857 | 0.0488202 | 0.1915253 | 0.0000000 | 0.0000000 | 0.8055329 | 0.0000000 | 0.0244101 | 0.0000000 | 1.1641734 | 0.3342304 | 0.9613819 | 0.2027915 | 1.1641734 | MULTIPOLYGON (((-132703 403... |
| MC CLELLAN MHP | CA3400179 | {E3BF3C3E-D516-45B2-A5AD-6900F8D16715} | Water Service Area | P | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 199 | 700 | 82.1684510 | 3.4236855 | 1.9431728 | 0.0000000 | 2.9610253 | 3.4236855 | 6.7548389 | 11.3814409 | 1.9431728 | 1.5730447 | 1.3879806 | 3.1460893 | 1.2954486 | 15.4528506 | 19.7093244 | 0.0000000 | 7.7726913 | 3.7938136 | 1.5730447 | 9.3457360 | 0.8327884 | 0.8327884 | 24.5209905 | 33.8667264 | 26.6492274 | 48.3017246 | 42.8423343 | 64.6798956 | 51.9104741 | 0.0000000 | 1.6655767 | 0.0000000 | 269.3607669 | 108.2624862 | 168.4083118 | 100.9524550 | 269.3607669 | MULTIPOLYGON (((-119809.2 7... |
| MAGNOLIA MUTUAL WATER | CA3400130 | {BADB48CD-AF05-4135-8FD5-9990BE415405} | Water Service Area | P | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 34 | 40 | 0.3600789 | 0.0024719 | 0.0090638 | 0.0148316 | 0.0140076 | 0.0131837 | 0.0000000 | 0.0205995 | 0.0090638 | 0.0000000 | 0.0230714 | 0.0222474 | 0.0090638 | 0.1384285 | 0.0000000 | 0.0609745 | 0.0230714 | 0.0000000 | 0.0000000 | 0.1235969 | 0.0230714 | 0.0230714 | 0.0494387 | 0.0650943 | 0.0296632 | 0.1870433 | 0.0552066 | 0.0000000 | 0.4152855 | 0.0000000 | 0.0337831 | 0.0000000 | 0.9310964 | 0.4268212 | 0.7844281 | 0.1466683 | 0.9310964 | MULTIPOLYGON (((-137015.5 3... |
| KORTHS PIRATES LAIR | CA3400135 | {7A556C0E-57E2-499A-9B87-B5707C837DA8} | Water Service Area | P | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 64 | 150 | 0.0489835 | 0.0062708 | 0.0081639 | 0.0000000 | 0.0014198 | 0.0024847 | 0.0027213 | 0.0018931 | 0.0005916 | 0.0004733 | 0.0022480 | 0.0013015 | 0.0000000 | 0.0067441 | 0.0035495 | 0.0026030 | 0.0085189 | 0.0035495 | 0.0035495 | 0.0084005 | 0.0031946 | 0.0008282 | 0.0301710 | 0.0069807 | 0.0069807 | 0.0104119 | 0.0000000 | 0.0028396 | 0.0034312 | 0.0000000 | 0.0031946 | 0.0000000 | 0.0879099 | 0.0784445 | 0.0563192 | 0.0315908 | 0.0879099 | MULTIPOLYGON (((-137297.1 1... |
| EL DORADO MOBILE HOME PARK | CA3400121 | {23B9391F-B6E7-44CE-A0EA-C2EE522857BF} | Water Service Area | P | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 128 | 256 | 48.2151375 | 0.0000000 | 9.7199081 | 4.3382641 | 0.0000000 | 0.0000000 | 3.6792873 | 5.6562177 | 0.7688063 | 0.0000000 | 7.5233187 | 1.4826978 | 7.2487450 | 0.4393179 | 0.9335505 | 0.8237210 | 5.6013030 | 0.0000000 | 0.0000000 | 3.1850546 | 5.3267293 | 5.3267293 | 9.6649934 | 17.1883121 | 9.6649934 | 35.3650894 | 18.5062657 | 14.6073196 | 84.0195448 | 0.0000000 | 11.4222649 | 0.0000000 | 139.4285126 | 10.8731176 | 78.8026450 | 59.7472319 | 138.5498768 | MULTIPOLYGON (((-124533.7 5... |
| RIVER'S EDGE MARINA & RESORT | CA3400107 | {F56E068C-AE1B-4C68-A2AF-F5058871E4E2} | Water Service Area | P | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 83 | 150 | 0.0782305 | 0.0100150 | 0.0130384 | 0.0000000 | 0.0022676 | 0.0039682 | 0.0043461 | 0.0030234 | 0.0009448 | 0.0007559 | 0.0035903 | 0.0020786 | 0.0000000 | 0.0107709 | 0.0056689 | 0.0041572 | 0.0136053 | 0.0056689 | 0.0056689 | 0.0134163 | 0.0051020 | 0.0013227 | 0.0481854 | 0.0111488 | 0.0111488 | 0.0166287 | 0.0000000 | 0.0045351 | 0.0054799 | 0.0000000 | 0.0051020 | 0.0000000 | 0.1403991 | 0.1252821 | 0.0899462 | 0.0504530 | 0.1403991 | MULTIPOLYGON (((-141320.7 1... |
| LAGUNA VILLAGE RV PARK | CA3400397 | {5184D6CC-8084-494D-BD81-B0009F76F1FD} | Water Service Area | P | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 28 | 32 | 6.6830099 | 0.3588648 | 0.0000000 | 0.8057530 | 0.8531502 | 0.0947945 | 0.2099020 | 0.1963600 | 0.1151076 | 0.0000000 | 0.2708413 | 0.2911544 | 0.1083365 | 0.7041875 | 1.3745198 | 0.6432482 | 0.6567902 | 1.2729543 | 0.4807434 | 2.8302919 | 0.1625048 | 0.0067710 | 1.0562812 | 0.7380426 | 0.3317806 | 2.7964368 | 10.6711486 | 1.4760853 | 2.5933058 | 0.0000000 | 2.1193334 | 1.6995294 | 20.2792449 | 1.7198425 | 17.8890701 | 2.3901748 | 20.2792449 | MULTIPOLYGON (((-122362.2 4... |
| SPINDRIFT MARINA | CA3400169 | {3B1EC48B-B696-452E-A769-99EF03DE955E} | Water Service Area | P | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 50 | 100 | 0.0954742 | 0.0122225 | 0.0159124 | 0.0000000 | 0.0027674 | 0.0048429 | 0.0053041 | 0.0036898 | 0.0011531 | 0.0009225 | 0.0043817 | 0.0025368 | 0.0000000 | 0.0131450 | 0.0069184 | 0.0050735 | 0.0166042 | 0.0069184 | 0.0069184 | 0.0163736 | 0.0062266 | 0.0016143 | 0.0588066 | 0.0136062 | 0.0136062 | 0.0202940 | 0.0000000 | 0.0055347 | 0.0066878 | 0.0000000 | 0.0062266 | 0.0000000 | 0.1713463 | 0.1528972 | 0.1097723 | 0.0615740 | 0.1713463 | MULTIPOLYGON (((-140144.2 1... |
| SAC CITY MOBILE HOME COMMUNITY LP | CA3400296 | {BBEBB4AF-57F6-4CBA-9746-6EDDFF0BB86B} | Water Service Area | P | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 164 | 350 | 89.4406295 | 3.7479883 | 16.1844949 | 4.2590776 | 0.0000000 | 9.0292445 | 9.8810600 | 7.6663397 | 0.0000000 | 0.0000000 | 4.2590776 | 2.0443572 | 6.6441611 | 1.3629048 | 13.2883221 | 0.0000000 | 11.0736018 | 1.5332679 | 1.5332679 | 3.5776252 | 1.5332679 | 0.0000000 | 15.3326793 | 40.7167818 | 30.1542694 | 70.5303250 | 122.8317979 | 7.4959766 | 81.7742899 | 0.0000000 | 0.0000000 | 0.0000000 | 229.3087378 | 17.2066735 | 118.9134465 | 110.3952913 | 229.3087378 | MULTIPOLYGON (((-124542.8 5... |
| ORANGE VALE WATER COMPANY | CA3410016 | {E7FB0496-8E98-4C1C-B073-40B7F2308EE3} | Water Service Area | P | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 5684 | 18005 | 6595.2837104 | 900.7356837 | 111.3937778 | 625.6077965 | 677.8935478 | 60.8572084 | 93.5577120 | 225.5184953 | 57.9795142 | 273.6232058 | 119.6327762 | 180.8290498 | 371.6355706 | 751.7849002 | 989.6690621 | 765.6465676 | 388.9188424 | 1021.3048996 | 452.6481914 | 3246.4149049 | 314.6881475 | 184.8067075 | 1686.0642367 | 693.1235814 | 304.7507104 | 1662.8045688 | 632.9109666 | 241.1577329 | 2657.5821267 | 180.8759821 | 1281.4642832 | 85.6664824 | 17387.1804880 | 12307.5229140 | 15384.0773580 | 1903.9653820 | 17288.0427400 | MULTIPOLYGON (((-104831.8 7... |
| GOLDEN STATE WATER CO. - CORDOVA | CA3410015 | {D09864D9-0038-4D96-AA06-F1D0F4207324} | Water Service Area | P | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 14798 | 44928 | 18022.2724131 | 2565.3574304 | 481.6820516 | 1671.0168964 | 1948.4422260 | 309.5110295 | 496.0689166 | 480.3416859 | 437.3376210 | 389.0523221 | 469.4236925 | 597.8111431 | 1276.1636931 | 1691.6766283 | 2652.7212636 | 2046.7106756 | 508.9551374 | 2173.8952361 | 835.7380588 | 7379.6730993 | 364.1246811 | 201.1308054 | 3505.8045695 | 2744.1803381 | 1410.0874409 | 7136.7947443 | 6049.7720444 | 3982.3063639 | 9008.5815107 | 229.0617289 | 2614.8681494 | 187.9680300 | 48114.9170297 | 26042.3592023 | 43427.4521762 | 4408.0063561 | 47835.4585323 | MULTIPOLYGON (((-105024.5 6... |
| HOLIDAY MOBILE VILLAGE | CA3400335 | {31419106-A98A-447B-AAF5-674FA6C8D17F} | Water Service Area | P | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 115 | 200 | 16.1279783 | 0.5855042 | 1.0379392 | 0.4258212 | 0.0000000 | 0.0000000 | 1.1177807 | 0.3459797 | 0.8782562 | 4.6840333 | 0.5588903 | 0.3992074 | 0.0000000 | 2.4218581 | 1.8097401 | 0.0000000 | 1.8629678 | 0.3992074 | 0.0000000 | 2.4750858 | 1.1177807 | 0.7718009 | 1.9960369 | 5.7219725 | 3.8323909 | 11.6568556 | 14.9835838 | 3.2735006 | 17.8312632 | 0.0000000 | 3.0605900 | 0.0000000 | 46.1217599 | 6.9728223 | 35.8222093 | 10.2995505 | 46.1217599 | MULTIPOLYGON (((-123885.3 5... |
| SOUTHWEST TRACT W M D [SWS] | CA3400156 | {0B3A9348-87E2-475F-B9EB-8A0E7658CA58} | Water Service Area | L | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 33 | 150 | 56.6169348 | 0.0000000 | 2.0808674 | 1.3005421 | 2.0808674 | 6.9362248 | 0.0000000 | 7.1963332 | 0.0000000 | 0.0000000 | 10.2309316 | 11.6181765 | 3.1213012 | 2.4276787 | 4.5952489 | 4.4218433 | 0.6069197 | 1.0404337 | 0.0000000 | 3.2080040 | 0.0000000 | 0.0000000 | 8.3234697 | 28.6986300 | 7.0229276 | 45.0854611 | 74.8245248 | 23.7565699 | 28.7853328 | 2.6877871 | 0.0000000 | 1.0404337 | 173.5790252 | 42.4843768 | 135.6898973 | 37.8891279 | 173.5790252 | MULTIPOLYGON (((-125846.9 5... |
| CARMICHAEL WATER DISTRICT | CA3410004 | {2E757010-FDE6-4A95-9D60-16E621A3CEAC} | Water Service Area | L | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 11704 | 37897 | 15937.0599353 | 1724.3160580 | 533.5977949 | 1199.7655014 | 1678.4193378 | 512.5932529 | 471.9441689 | 397.9273383 | 607.1599246 | 521.5633164 | 683.9909627 | 540.5514556 | 996.3263051 | 1594.7356744 | 1782.2476094 | 2121.9683481 | 569.9528868 | 1399.3542055 | 668.5650482 | 5256.1106375 | 358.0036122 | 177.4504952 | 3146.6518891 | 4055.8665545 | 2068.2998480 | 7534.2974087 | 3325.6253424 | 2229.6808268 | 6192.4682836 | 68.2012179 | 2115.7038660 | 295.2404645 | 39253.0451443 | 25026.1251431 | 33699.9843977 | 5000.0233004 | 38700.0076981 | MULTIPOLYGON (((-112199 690... |
| SCWA - ARDEN PARK VISTA | CA3410002 | {0FF3353C-7B37-44EC-A4B4-AB9A9D8BF79C} | Water Service Area | L | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 3043 | 10035 | 3303.3321668 | 464.8463772 | 36.2252598 | 207.8772002 | 416.0024649 | 48.4429082 | 77.3408834 | 64.6262639 | 37.7691910 | 17.5275827 | 48.6809905 | 161.5983651 | 138.5712947 | 186.9749090 | 253.0854846 | 1065.0769123 | 78.6860792 | 520.3241678 | 111.8599784 | 1822.9354385 | 76.4172240 | 22.9130304 | 673.1932014 | 384.0582803 | 225.3146454 | 807.2035269 | 396.0833955 | 269.6126776 | 989.6145807 | 11.8688948 | 394.6749078 | 8.2922534 | 8085.7219452 | 6015.5752354 | 7514.9400515 | 523.1232757 | 8038.0633273 | MULTIPOLYGON (((-120321.1 6... |
| SCWA - LAGUNA/VINEYARD | CA3410029 | {0D088865-F70E-43D5-899E-1B988AB097B9} | Water Service Area | L | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 47411 | 172666 | 45136.7375015 | 5322.5294772 | 665.6645745 | 5057.0689734 | 6577.5987020 | 742.3252838 | 877.9535408 | 838.5620300 | 1336.4151580 | 850.4024099 | 788.0623175 | 752.0385993 | 2362.8780777 | 3198.0928072 | 6037.4843391 | 8037.9755292 | 1691.6856820 | 7231.9926132 | 2915.5290405 | 24581.2965388 | 860.9255708 | 470.9663263 | 7878.0360736 | 6368.0895369 | 3336.6734065 | 12677.4048891 | 50411.1846769 | 16568.2152204 | 27501.6390185 | 245.7524857 | 10051.7754887 | 2220.3008068 | 145495.0217780 | 38496.1540810 | 130488.6918979 | 14709.7353786 | 145198.4272765 | MULTIPOLYGON (((-125203.9 4... |
| RIO COSUMNES CORRECTIONAL CENTER [SWS] | CA3400229 | {887BC04D-196C-4104-AF6A-DE128C8105D0} | Water Service Area | L | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 13 | 2800 | 1.2918032 | 0.6297541 | 0.0000000 | 0.2422131 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.3068033 | 0.0000000 | 0.0000000 | 0.0000000 | 0.1130328 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.8719672 | 0.0000000 | 0.0000000 | 0.1130328 | 0.3068033 | 0.0000000 | 0.3068033 | 1.0011475 | 3.7462292 | 5.7323766 | 0.6620491 | 2.3736884 | 0.4036885 | 22.2674574 | 8.3482781 | 4.4567210 | 0.0000000 | 4.4567210 | MULTIPOLYGON (((-124028.8 3... |
| SCWA MATHER-SUNRISE | CA3410704 | {4E3FC0F8-4DB3-4B03-AAEB-C965EE668712} | Water Service Area | L | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 6921 | 22839 | 5503.4347766 | 644.6310835 | 34.5914763 | 754.6721725 | 1002.5876562 | 96.5328507 | 57.1333078 | 68.4852856 | 38.5727314 | 11.5056903 | 19.8169793 | 35.6752369 | 188.7925174 | 320.4496478 | 533.0655801 | 1469.0347467 | 227.8878142 | 881.1934264 | 266.4397612 | 3755.5193696 | 60.4771366 | 43.0746683 | 855.3673715 | 318.0997475 | 166.9204055 | 892.5480355 | 4506.7663439 | 1552.9032678 | 2708.2807580 | 22.6098670 | 1180.2420790 | 163.6341130 | 18248.9313856 | 8114.4949570 | 17205.7757805 | 1005.1926521 | 18210.9684327 | MULTIPOLYGON (((-108416.1 5... |
| TUNNEL TRAILER PARK | CA3400192 | {896615F1-429B-4829-8D45-45AF249034BC} | Water Service Area | P | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 21 | 44 | 0.0165565 | 0.0017649 | 0.0000000 | 0.0000000 | 0.0094128 | 0.0000000 | 0.0014287 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0026053 | 0.0000000 | 0.0013447 | 0.0000000 | 0.0000000 | 0.0026894 | 0.0000000 | 0.0076479 | 0.0006723 | 0.0000000 | 0.0056309 | 0.0000000 | 0.0000000 | 0.0032777 | 0.0022692 | 0.0000000 | 0.0242885 | 0.0000000 | 0.0052107 | 0.0000000 | 0.0488291 | 0.0170608 | 0.0488291 | 0.0000000 | 0.0488291 | MULTIPOLYGON (((-136156.2 2... |
| IMPERIAL MANOR MOBILEHOME COMMUNITY | CA3400190 | {1F6AA119-33B3-4518-92C8-62843A00AE53} | Water Service Area | P | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 186 | 200 | 124.3703460 | 0.0000000 | 25.9994950 | 0.0000000 | 0.0000000 | 17.5734656 | 2.8363085 | 0.0704618 | 15.5996970 | 7.4411127 | 4.5053287 | 6.1453352 | 0.9454362 | 3.7817447 | 28.8358035 | 6.3816942 | 4.2544628 | 0.0000000 | 0.0000000 | 8.9816437 | 36.8720111 | 34.0357025 | 88.8710010 | 26.5177012 | 22.0123725 | 26.5177012 | 6.1453352 | 0.9454362 | 52.2207374 | 0.0000000 | 21.0504619 | 0.0000000 | 209.4594975 | 129.0975269 | 164.4684875 | 44.9910101 | 209.4594975 | MULTIPOLYGON (((-115157 740... |
| CALAM - ISLETON | CA3410012 | {24CB80A8-8A7C-46E9-8413-3346147E7444} | Water Service Area | P | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 480 | 1581 | 16.2336094 | 2.9637515 | 1.2133506 | 0.9302355 | 0.4914932 | 0.4448952 | 0.6812770 | 0.7280104 | 0.3235602 | 0.9302355 | 0.7280104 | 0.1617801 | 2.1178443 | 1.1729056 | 0.7577787 | 1.4155757 | 1.1729056 | 3.7400335 | 1.1324606 | 6.1829762 | 1.8551326 | 1.6178008 | 6.5225515 | 0.8089004 | 0.6066753 | 3.5280817 | 1.5340607 | 0.0000000 | 14.1701897 | 0.0000000 | 0.7574168 | 0.0000000 | 33.6922872 | 17.2306199 | 26.6548535 | 7.0374337 | 33.6922872 | MULTIPOLYGON (((-138559.3 1... |
| FOLSOM, CITY OF - ASHLAND | CA3410030 | {C83A0A46-997F-44BF-9441-C2DBC0D706CF} | Water Service Area | L | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 1079 | 3538 | 1799.7030393 | 132.3166375 | 17.2265963 | 79.8748540 | 122.5819340 | 103.5596466 | 42.9221364 | 33.7589226 | 209.0008907 | 103.1571051 | 73.6177167 | 43.3463904 | 43.3896373 | 158.0481682 | 247.7733397 | 345.3208615 | 43.8082024 | 163.9652512 | 90.3906967 | 593.7964348 | 367.6575353 | 81.6863124 | 847.4360028 | 195.9003890 | 74.3934966 | 358.4706017 | 125.1620483 | 43.1209906 | 317.7230275 | 1.0472024 | 422.7225186 | 0.6283737 | 3844.6819382 | 2934.2777772 | 3636.5160126 | 143.3077376 | 3779.8237502 | MULTIPOLYGON (((-100505 774... |
| LOCKE WATER WORKS CO [SWS] | CA3400138 | {66BAF16E-A801-4313-B7B8-FF4E564CA9F6} | Water Service Area | P | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 44 | 80 | 0.4115898 | 0.0028256 | 0.0103604 | 0.0169534 | 0.0160115 | 0.0150696 | 0.0000000 | 0.0235463 | 0.0103604 | 0.0000000 | 0.0263719 | 0.0254300 | 0.0103604 | 0.1582313 | 0.0000000 | 0.0696971 | 0.0263719 | 0.0000000 | 0.0000000 | 0.1412780 | 0.0263719 | 0.0263719 | 0.0565112 | 0.0744064 | 0.0339067 | 0.2138006 | 0.0631042 | 0.0000000 | 0.4746939 | 0.0000000 | 0.0386160 | 0.0000000 | 1.0642940 | 0.4878799 | 0.8966441 | 0.1676498 | 1.0642940 | MULTIPOLYGON (((-131976.7 2... |
| DEL PASO MANOR COUNTY WATER DI | CA3410007 | {BC88D650-D07A-47DF-A872-CD3F6142791A} | Water Service Area | L | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 1796 | 4520 | 2221.9390972 | 170.5870031 | 45.3809110 | 120.2825323 | 346.5586719 | 53.7173908 | 66.3032589 | 21.4396308 | 51.3430451 | 66.0596461 | 237.1084233 | 40.3903109 | 157.6807268 | 278.1739919 | 165.5702722 | 231.0331020 | 170.3101801 | 325.8336617 | 188.7334656 | 921.6068532 | 111.5401988 | 67.6595556 | 571.5015208 | 508.8525659 | 114.0279242 | 728.8307232 | 119.3773376 | 390.0212908 | 686.6403817 | 14.7405756 | 382.2746135 | 31.4497802 | 5591.9114660 | 3967.4074865 | 4970.7949939 | 620.9055619 | 5591.7005557 | MULTIPOLYGON (((-120274.9 6... |
| EAST WALNUT GROVE [SWS] | CA3400106 | {6E5DA14E-1740-4320-A7E4-FD9B48240017} | Water Service Area | L | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 166 | 300 | 1.3439937 | 0.0092265 | 0.0338305 | 0.0553590 | 0.0522835 | 0.0492080 | 0.0000000 | 0.0768875 | 0.0338305 | 0.0000000 | 0.0861140 | 0.0830385 | 0.0338305 | 0.5166841 | 0.0000000 | 0.2275870 | 0.0861140 | 0.0000000 | 0.0000000 | 0.4613251 | 0.0861140 | 0.0861140 | 0.1845300 | 0.2429645 | 0.1107180 | 0.6981386 | 0.2060585 | 0.0000000 | 1.5500522 | 0.0000000 | 0.1260955 | 0.0000000 | 3.4753156 | 1.5931093 | 2.9278765 | 0.5474391 | 3.4753156 | MULTIPOLYGON (((-132506.6 2... |
| FOLSOM STATE PRISON | CA3410032 | {CFEFFCD1-76A3-4403-A906-D04B910D61C5} | Water Service Area | S | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 2790 | 9703 | 22.5426613 | 4.1517526 | 0.0000000 | 4.0329731 | 12.1469937 | 0.1064474 | 0.0085195 | 0.0001474 | 0.0049313 | 0.0005898 | 0.0000000 | 0.0078093 | 0.2240872 | 0.1126992 | 0.2620161 | 1.3895998 | 0.0940949 | 0.9386397 | 0.1192110 | 3.1453984 | 0.1115472 | 0.0004353 | 0.4520514 | 0.0036714 | 0.0002949 | 18.9452115 | 69.8242689 | 1389.7446286 | 1256.9364067 | 56.6870957 | 76.7652684 | 33.8547933 | 3535.5532366 | 651.7407750 | 28.1916388 | 0.6348719 | 28.8265107 | MULTIPOLYGON (((-99838.11 7... |
| CALAM - ARDEN | CA3410045 | {FC35B709-C658-4890-B64C-374368BFAC79} | Water Service Area | P | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 1185 | 3908 | 3823.4030385 | 227.8237249 | 258.6358544 | 148.0773037 | 62.2606019 | 239.2704507 | 167.2117959 | 318.9373630 | 189.7774321 | 141.9916915 | 236.0923455 | 206.5761768 | 440.1718547 | 393.6199161 | 534.5491374 | 57.5818264 | 200.8255635 | 84.3191682 | 45.9095962 | 264.6683172 | 8.0957445 | 3.1275988 | 132.9715104 | 2124.1154199 | 1169.6691535 | 3425.7632110 | 1082.1423869 | 1976.9512017 | 3432.8439086 | 70.2029554 | 1099.6966575 | 58.7226100 | 10112.3147972 | 2391.7550772 | 6903.8350964 | 3129.7143646 | 10033.5494610 | MULTIPOLYGON (((-123013.9 6... |
| EDGEWATER MOBILE HOME PARK | CA3400433 | {FA333DDB-BCD6-4D29-9D40-664577E929C9} | Water Service Area | P | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 22 | 40 | 0.0323964 | 0.0041474 | 0.0053994 | 0.0000000 | 0.0009390 | 0.0016433 | 0.0017998 | 0.0012520 | 0.0003913 | 0.0003130 | 0.0014868 | 0.0008608 | 0.0000000 | 0.0044604 | 0.0023476 | 0.0017215 | 0.0056342 | 0.0023476 | 0.0023476 | 0.0055559 | 0.0021128 | 0.0005478 | 0.0199543 | 0.0046169 | 0.0046169 | 0.0068862 | 0.0000000 | 0.0018781 | 0.0022693 | 0.0000000 | 0.0021128 | 0.0000000 | 0.0581414 | 0.0518812 | 0.0372481 | 0.0208933 | 0.0581414 | MULTIPOLYGON (((-153715.8 7... |
| CALAM - LINCOLN OAKS | CA3410013 | {488DCE2C-6EEA-4377-8984-5833F5D03CDF} | Water Service Area | P | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 14390 | 47487 | 15621.3809893 | 1888.7872953 | 375.3828366 | 1272.1217031 | 1555.1540910 | 308.1458139 | 622.2810854 | 488.2818455 | 616.3732436 | 585.1829345 | 628.7492052 | 645.4504947 | 1034.6867979 | 1640.6899168 | 2442.1704417 | 778.1108146 | 739.8124694 | 2670.6595161 | 919.4308898 | 7390.0598679 | 502.5707184 | 297.5609445 | 3331.7506863 | 2522.8453244 | 1302.1867347 | 4899.5704351 | 2706.1128106 | 1486.1640154 | 9056.1812738 | 142.9928184 | 2708.1823079 | 287.6668389 | 42916.4902280 | 26529.1901631 | 38749.4769263 | 4073.9481050 | 42823.4250313 | MULTIPOLYGON (((-113737.5 7... |
| VIEIRA'S RESORT, INC | CA3400164 | {C8E1FF09-C1ED-47D9-BD1C-AE3C754ABA72} | Water Service Area | P | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 107 | 150 | 2.1367242 | 0.3992300 | 0.1686888 | 0.1293280 | 0.0000000 | 0.0618525 | 0.0843444 | 0.1012133 | 0.0449837 | 0.1293280 | 0.1012133 | 0.0224918 | 0.2755250 | 0.1630658 | 0.0955903 | 0.1968035 | 0.1630658 | 0.5004433 | 0.1574428 | 0.8040830 | 0.2530331 | 0.2249183 | 0.8659356 | 0.1124592 | 0.0843444 | 0.4667055 | 0.1968035 | 0.0000000 | 1.7937237 | 0.0000000 | 0.0674755 | 0.0000000 | 4.3296779 | 2.2716752 | 3.3512832 | 0.9783948 | 4.3296779 | MULTIPOLYGON (((-143708.1 1... |
| FLORIN COUNTY WATER DISTRICT | CA3410033 | {7D72D48E-DB2F-4E79-B396-70EEB030D102} | Water Service Area | L | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 2323 | 7831 | 2755.4087157 | 296.9354278 | 124.6257543 | 214.6215268 | 142.8389081 | 53.3547129 | 154.3761991 | 102.8638213 | 46.0111239 | 85.7880801 | 175.8361672 | 224.2674437 | 257.8035909 | 222.7155372 | 432.0027291 | 137.0102004 | 84.3574930 | 425.6666406 | 89.9994530 | 980.7724658 | 48.5268687 | 27.7142782 | 674.7293649 | 475.7493760 | 259.6899077 | 1099.9068850 | 2742.5834148 | 1394.4881155 | 2963.2330239 | 6.6336612 | 430.3204693 | 865.7716405 | 9951.0366937 | 1548.0063685 | 8549.7113979 | 1284.8842784 | 9834.5956763 | MULTIPOLYGON (((-119638.3 5... |
| WESTERNER MOBILE HOME PARK | CA3400331 | {5DF48C4F-72B2-40C9-B2F7-10F9FF855DCC} | Water Service Area | P | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 49 | 65 | 9.8864417 | 1.7859379 | 0.0000000 | 0.1093431 | 0.6925065 | 0.1093431 | 0.1093431 | 0.6651707 | 0.1366789 | 0.4373725 | 0.6104992 | 0.0000000 | 2.1504150 | 0.9111928 | 0.9476405 | 0.1731266 | 1.0478717 | 1.8679452 | 0.8565212 | 3.9090170 | 0.3280294 | 0.3280294 | 0.8565212 | 3.4260849 | 1.7312663 | 5.1209035 | 9.9411133 | 8.9752489 | 5.5764998 | 0.1731266 | 1.4487965 | 0.0000000 | 31.7003970 | 5.5856118 | 23.8276913 | 7.4262212 | 31.2539125 | MULTIPOLYGON (((-122693.1 4... |
| EL DORADO WEST MHP | CA3400122 | {2667CD45-9C2C-41B7-A95B-2F343B570215} | Water Service Area | P | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 128 | 172 | 51.1665127 | 0.0000000 | 10.3148892 | 4.6038206 | 0.0000000 | 0.0000000 | 3.9045061 | 6.0024497 | 0.8158669 | 0.0000000 | 7.9838408 | 1.5734577 | 7.6924598 | 0.4662097 | 0.9906956 | 0.8741432 | 5.9441735 | 0.0000000 | 0.0000000 | 3.3800202 | 5.6527924 | 5.6527924 | 10.2566130 | 18.2404539 | 10.2566130 | 37.5298795 | 19.6390829 | 15.5014720 | 89.1626019 | 0.0000000 | 12.1214518 | 0.0000000 | 147.9632982 | 11.5386897 | 83.6263619 | 63.4045169 | 147.0308788 | MULTIPOLYGON (((-124742.1 5... |
| TOKAY PARK WATER CO | CA3400172 | {D2908483-C728-45ED-9A75-BA27D6E66685} | Water Service Area | P | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 198 | 525 | 172.9103108 | 13.8638644 | 1.8971726 | 3.7737854 | 10.4172265 | 2.5212446 | 20.5379761 | 0.0041485 | 0.0064015 | 12.6022626 | 13.3274782 | 10.3961842 | 17.9662119 | 27.3715141 | 36.0110885 | 0.0027623 | 2.2109893 | 37.8735331 | 11.3805327 | 80.9724226 | 0.0051347 | 0.0011499 | 43.7650747 | 32.2708872 | 12.0942179 | 48.1728135 | 239.0565210 | 36.5633959 | 213.6839340 | 0.0000000 | 28.3202696 | 0.0132022 | 651.5003569 | 133.8630344 | 538.8538080 | 112.6465489 | 651.5003569 | MULTIPOLYGON (((-122789.8 5... |
| LAGUNA DEL SOL INC | CA3400181 | {F64FE571-B7DD-4F78-BBCF-2057DA131C24} | Water Service Area | P | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 112 | 470 | 8.9804190 | 0.3985393 | 0.8767865 | 0.3985393 | 0.7439400 | 0.9033558 | 0.4251086 | 0.3985393 | 0.0000000 | 0.2656929 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 1.9926965 | 2.4178051 | 0.1594157 | 1.7004344 | 1.7004344 | 4.8621795 | 0.0000000 | 0.0000000 | 2.5240823 | 0.3985393 | 0.3985393 | 1.5941572 | 0.3454007 | 0.0000000 | 5.1013031 | 0.1594157 | 0.2656929 | 0.0000000 | 23.6732346 | 17.8014222 | 22.1587853 | 1.5144494 | 23.6732346 | MULTIPOLYGON (((-105025.5 4... |
| OLYMPIA MOBILODGE | CA3410022 | {B1E1B46F-9300-473E-B970-1D739E004B14} | Water Service Area | P | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 200 | 450 | 114.4237404 | 2.6713714 | 0.2226143 | 11.7985569 | 5.1201285 | 6.4558141 | 10.0176426 | 8.9045712 | 3.3392142 | 13.1342426 | 0.0000000 | 0.0000000 | 10.0176426 | 18.6995996 | 7.5688856 | 5.3427427 | 11.1307140 | 21.5935853 | 9.7950284 | 30.7207708 | 12.2437855 | 10.0176426 | 50.7560561 | 8.9045712 | 7.3462713 | 32.9469136 | 101.2894979 | 18.2543710 | 69.9008842 | 0.0000000 | 3.1165999 | 16.0282282 | 289.8437939 | 81.2542126 | 221.9464382 | 67.8973557 | 289.8437939 | MULTIPOLYGON (((-123549.9 5... |
| GOLDEN STATE WATER CO - ARDEN WATER SERV | CA3410003 | {E2997C83-F848-48AF-8915-B45E6469AC6E} | Water Service Area | P | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 1716 | 5125 | 2173.3862508 | 131.7147921 | 82.2403920 | 171.7079195 | 140.8479724 | 19.2857784 | 140.6953734 | 52.8057022 | 172.6873796 | 34.4563047 | 178.9267558 | 37.1685200 | 138.6454528 | 351.1414354 | 319.0998779 | 182.5319559 | 19.4306387 | 238.5720694 | 123.1865478 | 727.7085408 | 0.2417045 | 0.2417045 | 130.6029265 | 599.3176233 | 334.9873262 | 1315.0747835 | 887.9273434 | 321.8426915 | 1705.9940680 | 0.0911940 | 742.0066805 | 10.7864072 | 6555.7696756 | 2887.1212910 | 4827.6115519 | 1625.5697248 | 6453.1812768 | MULTIPOLYGON (((-120307.3 6... |
| ELEVEN OAKS MOBILE HOME COMMUNITY | CA3400191 | {AB99BC88-8951-414B-BA24-E873AF46C168} | Water Service Area | P | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 136 | 262 | 71.2146352 | 2.9672765 | 1.6841299 | 0.0000000 | 2.5662932 | 2.9672765 | 5.8543563 | 9.8641893 | 1.6841299 | 1.3633432 | 1.2029499 | 2.7266865 | 1.1227533 | 13.3928424 | 17.0818888 | 0.0000000 | 6.7365195 | 3.2880631 | 1.3633432 | 8.0998628 | 0.7217700 | 0.7217700 | 21.2521152 | 29.3519780 | 23.0966384 | 41.8626572 | 37.1310541 | 56.0574662 | 44.9903269 | 0.0000000 | 1.4435399 | 0.0000000 | 233.4524808 | 93.8300936 | 145.9579234 | 87.4945574 | 233.4524808 | MULTIPOLYGON (((-119816 721... |
| CALAM - ANTELOPE | CA3410031 | {AE6C5C3E-859C-4CD7-B3DF-5913A953F8F9} | Water Service Area | P | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 10528 | 34720 | 10529.3295307 | 1500.7495311 | 184.1450913 | 1077.3930203 | 1157.7794652 | 100.9615090 | 122.3726286 | 116.4469685 | 468.5101697 | 247.5986494 | 368.3586872 | 449.4701379 | 737.4305999 | 1076.7061396 | 1668.6530780 | 937.3259723 | 315.4278826 | 1860.8089818 | 620.5036265 | 5544.0872962 | 184.3698474 | 106.3231597 | 1747.2662153 | 1678.4584065 | 649.2056543 | 3237.9760191 | 2947.3357188 | 3199.3980847 | 5245.2397355 | 112.5516151 | 2081.7481081 | 77.2668921 | 33119.7160488 | 19456.1758944 | 29644.8479042 | 3388.7925699 | 33033.6404741 | MULTIPOLYGON (((-123334.7 8... |
| CALIFORNIA STATE FAIR | CA3410026 | {C97F5649-348B-4378-9C32-3B6D3E82725B} | Water Service Area | S | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 269 | 650 | 285.4716540 | 20.7008685 | 13.0215140 | 11.0182042 | 17.0281337 | 8.3471244 | 5.3421596 | 9.3487793 | 13.6892840 | 2.0033099 | 0.0000000 | 23.3719483 | 28.7141079 | 30.0496478 | 34.7240374 | 3.3388498 | 64.7736852 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 177.2929219 | 95.4911030 | 285.4716540 | 48.4133214 | 91.1505983 | 78.1290842 | 0.0000000 | 52.4199411 | 0.0000000 | 532.2126508 | 262.0997057 | 373.9511725 | 151.9176638 | 525.8688363 | MULTIPOLYGON (((-125658 651... |
| PLANTATION MOBILE HOME PARK | CA3400401 | {6BF6AFC4-25D1-4131-BA1A-CE81F4561902} | Water Service Area | P | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 44 | 44 | 3.3251292 | 0.1207143 | 0.2139935 | 0.0877922 | 0.0000000 | 0.0000000 | 0.2304545 | 0.0713312 | 0.1810714 | 0.9657141 | 0.1152273 | 0.0823052 | 0.0000000 | 0.4993181 | 0.3731168 | 0.0000000 | 0.3840908 | 0.0823052 | 0.0000000 | 0.5102921 | 0.2304545 | 0.1591233 | 0.4115259 | 1.1797076 | 0.7901297 | 2.4033112 | 3.0891877 | 0.6749025 | 3.6762980 | 0.0000000 | 0.6310064 | 0.0000000 | 9.5089917 | 1.4375971 | 7.3855181 | 2.1234736 | 9.5089917 | MULTIPOLYGON (((-124338.4 5... |
| CALAM - PARKWAY | CA3410017 | {78A7BD82-DB23-4AC1-8736-574511C4CC84} | Water Service Area | P | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 14779 | 48738 | 17666.7886640 | 1633.5316867 | 753.3380936 | 1531.9788719 | 1545.6177541 | 514.1300586 | 712.7247277 | 694.3526534 | 639.9551980 | 713.2865314 | 700.4012000 | 727.1387473 | 1145.1392905 | 1918.3765008 | 2490.3823970 | 865.4412869 | 1080.9936661 | 2718.9202130 | 1049.2628405 | 7162.9956077 | 646.5433097 | 383.4428179 | 3418.0266877 | 3517.2627210 | 1917.1472603 | 7085.7663686 | 19228.1809918 | 6964.8736569 | 18664.8061541 | 20.8369935 | 3449.3939669 | 1385.9751800 | 58635.0073685 | 8920.9404253 | 48630.3167965 | 9803.7863660 | 58434.1031625 | MULTIPOLYGON (((-123335.4 5... |
| CITRUS HEIGHTS WATER DISTRICT | CA3410006 | {20EF35DA-A434-4A91-BE3D-7D996B8C6730} | Water Service Area | L | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 19940 | 65911 | 25632.8367034 | 2743.5767440 | 568.7994440 | 2331.8277629 | 2533.3929840 | 445.8866083 | 769.4115233 | 665.4069464 | 867.0779805 | 841.0558325 | 723.3234592 | 1164.6015213 | 1875.0627674 | 3057.1645880 | 3953.9411189 | 2080.4093464 | 1011.8980764 | 3552.5017711 | 1380.0948194 | 10344.1670582 | 554.2501347 | 285.7944238 | 4292.6223834 | 5758.6426763 | 2620.3849095 | 10996.0472617 | 2874.5225437 | 2092.1397411 | 12379.8740660 | 161.9306770 | 3185.5796728 | 70.8069763 | 68912.3666060 | 48147.5129291 | 61620.0399314 | 6961.1604963 | 68581.2004277 | MULTIPOLYGON (((-113645.4 7... |
| SEQUOIA WATER ASSOC | CA3400155 | {D4629F0D-8EAD-4402-88C8-D55C4121EA5B} | Water Service Area | P | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 18 | 54 | 0.0948332 | 0.0006510 | 0.0023871 | 0.0039062 | 0.0036892 | 0.0034722 | 0.0000000 | 0.0054252 | 0.0023871 | 0.0000000 | 0.0060763 | 0.0058593 | 0.0023871 | 0.0364576 | 0.0000000 | 0.0160587 | 0.0060763 | 0.0000000 | 0.0000000 | 0.0325514 | 0.0060763 | 0.0060763 | 0.0130206 | 0.0171438 | 0.0078123 | 0.0492612 | 0.0145396 | 0.0000000 | 0.1093729 | 0.0000000 | 0.0088974 | 0.0000000 | 0.2452209 | 0.1124110 | 0.2065932 | 0.0386277 | 0.2452209 | MULTIPOLYGON (((-136917.7 3... |
| FAIR OAKS WATER DISTRICT | CA3410009 | {797F9D1A-2D43-4FA9-882A-DAA2A539318A} | Water Service Area | L | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 14293 | 35114 | 14233.4070807 | 1447.1879511 | 332.3588693 | 1568.3188854 | 1874.6761780 | 113.0360901 | 229.1722624 | 208.1588999 | 391.4233194 | 205.9499753 | 468.9849244 | 293.0716923 | 803.5523705 | 1064.3255362 | 2213.6241800 | 2473.8514603 | 545.7144862 | 1871.7864644 | 844.6722781 | 7090.3860908 | 261.1826011 | 108.3058016 | 3092.2584650 | 1843.9238233 | 768.3349827 | 4050.7625248 | 1371.7849776 | 707.5508033 | 4654.7423927 | 93.9869062 | 2113.1426067 | 11.7674901 | 36002.5983189 | 27049.6231424 | 32923.4030065 | 2851.6924166 | 35775.0954231 | MULTIPOLYGON (((-107763.5 6... |
| RANCHO MARINA | CA3400149 | {A2E2869F-FE8D-48EF-8762-16238D0B3F9A} | Water Service Area | P | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 77 | 250 | 0.1521691 | 0.0194806 | 0.0253615 | 0.0000000 | 0.0044107 | 0.0077187 | 0.0084538 | 0.0058809 | 0.0018378 | 0.0014702 | 0.0069836 | 0.0040431 | 0.0000000 | 0.0209508 | 0.0110267 | 0.0080863 | 0.0264642 | 0.0110267 | 0.0110267 | 0.0260966 | 0.0099241 | 0.0025729 | 0.0937274 | 0.0216859 | 0.0216859 | 0.0323451 | 0.0000000 | 0.0088214 | 0.0106592 | 0.0000000 | 0.0099241 | 0.0000000 | 0.2730958 | 0.2436911 | 0.1749577 | 0.0981381 | 0.2730958 | MULTIPOLYGON (((-138162.3 1... |
| FREEPORT MARINA | CA3400125 | {1A2ECBCF-8086-4599-91D1-7BDC9F543BC8} | Water Service Area | P | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 27 | 42 | 1.0728029 | 0.1371620 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.1812498 | 0.0979729 | 0.0489864 | 0.1224661 | 0.1322634 | 0.1714525 | 0.0342905 | 0.1469593 | 0.1420607 | 0.0000000 | 0.1420607 | 0.1469593 | 0.1469593 | 0.4310806 | 0.2008444 | 0.1273647 | 0.4996616 | 0.0000000 | 0.0000000 | 2.1015181 | 0.0000000 | 0.0636824 | 0.0000000 | 3.0371590 | 0.8719586 | 2.5081055 | 0.5290535 | 3.0371590 | MULTIPOLYGON (((-130871.1 5... |
| HAPPY HARBOR (SWS) | CA3400128 | {95E0CED3-1431-4EF7-B516-D9DE2E6F03C4} | Water Service Area | P | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 45 | 60 | 0.0216151 | 0.0027671 | 0.0036025 | 0.0000000 | 0.0006265 | 0.0010964 | 0.0012008 | 0.0008354 | 0.0002611 | 0.0002088 | 0.0009920 | 0.0005743 | 0.0000000 | 0.0029760 | 0.0015663 | 0.0011486 | 0.0037591 | 0.0015663 | 0.0015663 | 0.0037069 | 0.0014097 | 0.0003655 | 0.0133136 | 0.0030804 | 0.0030804 | 0.0045945 | 0.0000000 | 0.0012530 | 0.0015141 | 0.0000000 | 0.0014097 | 0.0000000 | 0.0387923 | 0.0346155 | 0.0248521 | 0.0139402 | 0.0387923 | MULTIPOLYGON (((-139870.3 1... |
| FOLSOM, CITY OF - MAIN | CA3410014 | {6E4D1E97-03EB-48F4-9F64-E74E49CC4CC2} | Water Service Area | L | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 21424 | 68122 | 22409.3823384 | 2382.4430031 | 218.1355460 | 1746.9283429 | 4083.1483087 | 390.0036513 | 476.8583096 | 417.5347017 | 283.0639620 | 329.3286579 | 373.3616549 | 451.4793379 | 670.3668140 | 1180.5443541 | 2255.4207366 | 6343.7663048 | 806.9986528 | 2727.7080877 | 1178.6318783 | 11490.6508104 | 237.1898408 | 146.3657554 | 3590.2477618 | 3009.7032962 | 1320.8580528 | 7328.4837662 | 12934.4170968 | 1693.2864940 | 8432.6283268 | 105.2228615 | 3897.3944960 | 176.8717260 | 62461.8523292 | 35222.0313281 | 58709.8582238 | 3405.2750719 | 62115.1332958 | MULTIPOLYGON (((-107067.9 6... |
| RANCHO MURIETA COMMUNITY SERVI | CA3410005 | {2C72EE8A-A081-49FB-8EEA-808BF54CDB58} | Water Service Area | L | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 2726 | 5744 | 1402.0967904 | 118.3930766 | 42.3473557 | 204.1395173 | 240.7495319 | 0.0000000 | 5.9102352 | 5.3262089 | 18.2285332 | 73.7524494 | 27.3517589 | 74.6522384 | 44.1562938 | 81.2814649 | 87.8793345 | 318.6065856 | 59.3222060 | 205.1382026 | 103.3669845 | 1029.1069175 | 63.0813034 | 57.1710682 | 269.5909676 | 40.5131013 | 39.9972505 | 103.3989053 | 187.8473482 | 120.1082782 | 661.4100294 | 6.9226010 | 105.7357819 | 0.0000000 | 3238.7025585 | 2156.6785197 | 3039.9096847 | 198.6877215 | 3238.5974062 | MULTIPOLYGON (((-95334.2 52... |
| CAL AM FRUITRIDGE VISTA | CA3410023 | {501904B9-FD46-4D56-89DE-D87AD8F5FD70} | Water Service Area | P | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 4667 | 15385 | 6899.9140996 | 458.8445334 | 339.2099413 | 235.1486797 | 287.1473267 | 520.9996164 | 262.7374008 | 367.1883343 | 301.6105218 | 359.1749480 | 354.9332453 | 565.0609345 | 692.3060889 | 876.2377458 | 783.8075573 | 141.4321445 | 354.0750809 | 745.4935970 | 344.8495494 | 1619.8296502 | 94.9132221 | 57.5416225 | 1236.1776014 | 2130.6340062 | 1058.7419859 | 4043.9068480 | 4075.1131280 | 2663.3011157 | 10938.9170749 | 121.1433988 | 1060.0683027 | 240.0083338 | 22602.6630640 | 3504.1117102 | 16546.1806283 | 6009.7140860 | 22555.8947143 | MULTIPOLYGON (((-125839.3 5... |
| B & W RESORT MARINA | CA3400103 | {CEC00BE2-7FBA-4B81-BE20-7D326EB6EBF6} | Water Service Area | P | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 37 | 100 | 0.0422931 | 0.0079021 | 0.0033389 | 0.0025598 | 0.0000000 | 0.0012243 | 0.0016695 | 0.0020034 | 0.0008904 | 0.0025598 | 0.0020034 | 0.0004452 | 0.0054536 | 0.0032276 | 0.0018921 | 0.0038954 | 0.0032276 | 0.0099055 | 0.0031163 | 0.0159156 | 0.0050084 | 0.0044519 | 0.0171398 | 0.0022260 | 0.0016695 | 0.0092377 | 0.0038954 | 0.0000000 | 0.0355040 | 0.0000000 | 0.0013356 | 0.0000000 | 0.0856992 | 0.0449643 | 0.0663334 | 0.0193658 | 0.0856992 | MULTIPOLYGON (((-138335.4 1... |
| CALAM - SUBURBAN ROSEMONT | CA3410010 | {C7B2A772-B03D-4109-86B0-996D6A191F5F} | Water Service Area | P | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 16238 | 53563 | 21045.1018353 | 2594.5943353 | 612.2587703 | 1593.9305166 | 1670.7351745 | 472.4194316 | 743.9075600 | 652.7686692 | 567.7706604 | 581.9992968 | 873.7032718 | 628.3855992 | 1288.6020796 | 2508.0974882 | 3438.3892872 | 1661.0922839 | 1156.4474108 | 2261.5294332 | 730.4776261 | 8262.3220973 | 438.6402189 | 271.0385123 | 3424.8520826 | 4520.7820567 | 2319.7944294 | 9357.9276554 | 6905.2987809 | 7724.7725462 | 13791.4166440 | 90.8097107 | 3942.4070344 | 380.2805737 | 57896.7439217 | 25061.7586318 | 49286.9627735 | 8374.3720099 | 57661.3347833 | MULTIPOLYGON (((-120054.6 5... |
| SAN JUAN WATER DISTRICT | CA3410021 | {5F6CA8E6-CCFE-44C8-978B-6012BDE0AD70} | Water Service Area | L | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 10672 | 29641 | 10750.1495842 | 853.9948037 | 168.3039321 | 876.1496099 | 1032.1228618 | 100.4248825 | 274.9765606 | 127.7463448 | 159.7577616 | 110.8699984 | 133.2796406 | 126.6536145 | 472.3792109 | 684.0889993 | 984.1402476 | 4256.4919483 | 388.7691676 | 1754.1276808 | 723.8699666 | 6209.8360395 | 527.7347496 | 356.6210547 | 2882.6803563 | 726.1057528 | 339.1581882 | 1657.6331884 | 2761.9691642 | 831.2463017 | 3408.5773626 | 286.8715750 | 1467.3490957 | 17.2561800 | 30122.4115704 | 21349.1418911 | 28296.8723978 | 1717.5297944 | 30014.4021922 | MULTIPOLYGON (((-100141.1 8... |
| ELK GROVE WATER SERVICE | CA3410008 | {E33EFAB9-03BB-4AA6-B34C-E1B2B887AC40} | Water Service Area | P | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 12882 | 42540 | 13238.9100963 | 1469.7831520 | 202.1735678 | 1385.9378896 | 1906.7785552 | 252.8846375 | 223.5107451 | 327.6343327 | 101.5057735 | 344.9642849 | 292.3949973 | 244.6466708 | 667.2277312 | 1116.8947162 | 1440.6413362 | 2832.0848266 | 429.8468798 | 1902.8226201 | 627.9585906 | 7551.9501142 | 282.8809066 | 112.6581718 | 2860.9103294 | 1594.5949046 | 863.8740389 | 2826.0496528 | 8938.9252240 | 3209.2685277 | 7656.1027968 | 69.7369659 | 2834.6937958 | 387.7772159 | 42646.7296258 | 19550.2250997 | 38993.5138540 | 3264.0175810 | 42257.5314350 | MULTIPOLYGON (((-113074.8 4... |
| DELTA CROSSING MHP | CA3400150 | {178C1128-CAD8-4118-8692-500FC1384637} | Water Service Area | P | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 22 | 30 | 0.0565275 | 0.0072273 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0095503 | 0.0051623 | 0.0025812 | 0.0064529 | 0.0069691 | 0.0090341 | 0.0018068 | 0.0077435 | 0.0074854 | 0.0000000 | 0.0074854 | 0.0077435 | 0.0077435 | 0.0227142 | 0.0105828 | 0.0067110 | 0.0263279 | 0.0000000 | 0.0000000 | 0.1107319 | 0.0000000 | 0.0033555 | 0.0000000 | 0.1600321 | 0.0459447 | 0.1321555 | 0.0278766 | 0.1600321 | MULTIPOLYGON (((-132728.4 4... |
| GALT, CITY OF | CA3410011 | {1EB5C880-698B-463D-916D-27EB4115BE84} | Water Service Area | L | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 7471 | 26536 | 6987.5357777 | 1096.3084285 | 168.1832539 | 504.4991186 | 788.6043595 | 243.2690568 | 210.1360339 | 140.5426889 | 341.9418512 | 160.7063475 | 347.3758689 | 152.2076332 | 550.4693882 | 686.5717245 | 807.1560200 | 650.1046848 | 139.4593192 | 907.4138072 | 523.4602820 | 3724.2608083 | 108.8264389 | 44.2709236 | 1454.3735494 | 906.4809387 | 414.1512468 | 1808.9014201 | 871.8607749 | 519.8596854 | 9314.4508950 | 22.4436804 | 789.0936920 | 20.0053925 | 21489.7769024 | 9952.0627822 | 19937.1926609 | 1404.1088571 | 21341.3015180 | MULTIPOLYGON (((-115624.6 2... |
| LINCOLN CHAN-HOME RANCH | CA3400137 | {8E59F56D-9D04-48D4-B6E4-F0584DA465D8} | Water Service Area | P | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 19 | 33 | 1.5038756 | 0.0103241 | 0.0378550 | 0.0619445 | 0.0585032 | 0.0550618 | 0.0000000 | 0.0860341 | 0.0378550 | 0.0000000 | 0.0963582 | 0.0929168 | 0.0378550 | 0.5781490 | 0.0000000 | 0.2546609 | 0.0963582 | 0.0000000 | 0.0000000 | 0.5162044 | 0.0963582 | 0.0963582 | 0.2064818 | 0.2718677 | 0.1238891 | 0.7811894 | 0.2305713 | 0.0000000 | 1.7344469 | 0.0000000 | 0.1410959 | 0.0000000 | 3.8887400 | 1.7826260 | 3.2761774 | 0.6125626 | 3.8887400 | MULTIPOLYGON (((-137040.3 3... |
| RIO LINDA/ELVERTA COMMUNITY WATER DIST | CA3410018 | {03B163CA-7B3B-4A23-8E31-77AF963C2BF3} | Water Service Area | L | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 4621 | 14381 | 3761.6033554 | 491.6445232 | 156.4692332 | 430.8331149 | 415.6196965 | 66.8600922 | 168.7735897 | 56.2689625 | 112.6547781 | 116.4832566 | 114.0660929 | 118.0282656 | 173.1294602 | 297.2131196 | 606.9213690 | 259.3021303 | 177.3356710 | 572.5923869 | 157.1920756 | 1918.2923459 | 114.4708846 | 47.4533275 | 772.9578664 | 519.2560180 | 340.4159673 | 1070.3531430 | 764.5025291 | 336.6403506 | 2585.2987230 | 16.7415040 | 512.2151088 | 21.2278122 | 11831.4050738 | 7594.7790460 | 10210.2384197 | 1618.8821031 | 11829.1205228 | MULTIPOLYGON (((-125758.1 7... |
| SACRAMENTO SUBURBAN WATER DISTRICT | CA3410001 | {91C3BEAC-ACF7-4419-A57D-679912168873} | Water Service Area | L | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 46573 | 184385 | 72505.3459272 | 6479.7222905 | 3001.4247113 | 4341.8155148 | 5488.0234146 | 3068.6350750 | 2883.7080242 | 3205.4749351 | 3100.4035000 | 3336.6907659 | 2893.3245566 | 2342.0597477 | 5541.2780373 | 6791.8950710 | 10036.8358151 | 6176.8449416 | 3817.2095264 | 7204.1308118 | 2837.1578291 | 23467.3263083 | 2086.9756387 | 1160.3317865 | 12037.3741485 | 21072.3795945 | 10273.7781043 | 37000.6454705 | 20601.5276174 | 17683.8035710 | 43046.5862171 | 834.1766712 | 12463.5266871 | 623.5010804 | 193125.5452987 | 97872.4234544 | 157585.1487857 | 33398.9027882 | 190984.0515740 | MULTIPOLYGON (((-115313.2 7... |
| CALAM - WALNUT GROVE | CA3410047 | {B1588BFA-E492-4087-BF44-72049EB4D5B5} | Water Service Area | P | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 197 | 651 | 4.6150009 | 0.0316819 | 0.1161671 | 0.1900916 | 0.1795309 | 0.1689703 | 0.0000000 | 0.2640161 | 0.1161671 | 0.0000000 | 0.2956980 | 0.2851374 | 0.1161671 | 1.7741880 | 0.0000000 | 0.7814876 | 0.2956980 | 0.0000000 | 0.0000000 | 1.5840964 | 0.2956980 | 0.2956980 | 0.6336386 | 0.8342908 | 0.3801831 | 2.3972659 | 0.7075631 | 0.0000000 | 5.3225640 | 0.0000000 | 0.4329864 | 0.0000000 | 11.9335264 | 5.4704130 | 10.0537320 | 1.8797944 | 11.9335264 | MULTIPOLYGON (((-132035.3 2... |
| CITY OF SACRAMENTO MAIN | CA3410020 | {8DB287D6-CBB3-484C-802F-FFC909F0959F} | Water Service Area | S | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 142794 | 510931 | 194000.0141638 | 20453.4979307 | 9400.8965823 | 15079.7381061 | 17439.0633155 | 6216.6682096 | 6406.5976684 | 5803.8609561 | 6254.8170189 | 6278.4908829 | 6139.2199311 | 6728.5413058 | 13349.2772832 | 17395.8895949 | 26982.4616746 | 20530.7727300 | 9540.2209738 | 21768.6901173 | 8217.1719086 | 67434.8107773 | 3476.4013912 | 1804.5854572 | 29856.8566750 | 47509.5512539 | 24524.2427584 | 96708.3467115 | 98585.0823322 | 62059.7559565 | 151210.5537799 | 1249.2624799 | 34334.3020476 | 9241.7219319 | 516189.1113701 | 159508.4328420 | 431797.3527101 | 77003.0770055 | 508800.4297156 | MULTIPOLYGON (((-128741.8 5... |
6.3 Clean & Format Results
We could stop here, and save the dataset containing the results to an output file (as is done in Section 6.5 ). But, it may be useful to do some additional computations and re-formatting before saving the dataset. For example, in this case it may be useful to calculate the racial/ethnic breakdown of each system’s population as percentages of the total population (in addition to the total counts derived above).
# We can start by renaming the fields that start with population_ and median_household_income to indicate that they are estimates.
# water_systems_sac_demographics <- water_systems_sac_demographics %>%
# rename_with(.fn = ~ str_replace(.,
# pattern = 'population_',
# replacement = 'population_')) # %>%
# rename_with(.fn = ~ str_replace(.,
# pattern = 'median_household_income',
# replacement = 'median_household_income'))# Then add columns with each racial/ethnic group's estimated percent of the total population within each water system's service area:
water_systems_sac_demographics <- water_systems_sac_demographics %>%
mutate(
across(
.cols = starts_with('population_'),
.fns = ~ round(.x / population_total * 100, 2),
.names = "{str_replace(.col, 'population_', 'percent_')}"
),
.after = population_white) We can also calculate the estimated poverty rate for each water system’s service area.
water_systems_sac_demographics <- water_systems_sac_demographics %>%
mutate(poverty_rate_percent =
round(100 * poverty_below_count / poverty_total_assessed,
2),
.before = poverty_above_count)Then format the data, rounding the estimated values to appropriate levels of precision:
water_systems_sac_demographics <- water_systems_sac_demographics %>%
mutate(
across(
.cols = starts_with('population_'),
.fns = ~ round(.x, 0)
)) # %>%
# mutate(median_household_income = round(median_household_income, digits = 0))Finally, just as a check, add a column (which we’ll call population_percent_difference) to compute the difference between the estimated total population (in the population_total field) and the total population listed in the POPULATION field (the reported value from the water system dataset).
water_systems_sac_demographics <- water_systems_sac_demographics %>%
mutate(population_percent_difference =
round((population_total - POPULATION) / POPULATION * 100, 2))
# rearrange fields to make order more logical
water_systems_sac_demographics <- water_systems_sac_demographics %>%
relocate(population_total, .after = 'POPULATION') %>%
relocate(population_percent_difference, .after = 'population_total') %>%
# relocate(median_household_income, geometry, .after = last_col()) %>%
select(-percent_total) # drop the percent_total field - it's always just "1"Here’s a view of the contents and structure of the re-formatted dataset:
glimpse(water_systems_sac_demographics)Rows: 62
Columns: 58
$ WATER_SY_1 <chr> "HOOD WATER MAINTENCE DIST…
$ WATER_SYST <chr> "CA3400101", "CA3400179", …
$ GLOBALID <chr> "{36268DB3-9DB2-4305-A85A-…
$ BOUNDARY_T <chr> "Water Service Area", "Wat…
$ OWNER_TYPE <chr> "L", "P", "P", "P", "P", "…
$ COUNTY <chr> "SACRAMENTO", "SACRAMENTO"…
$ REGULATING <chr> "LPA64 - SACRAMENTO COUNTY…
$ FEDERAL_CL <chr> "COMMUNITY", "COMMUNITY", …
$ STATE_CLAS <chr> "COMMUNITY", "COMMUNITY", …
$ SERVICE_CO <dbl> 82, 199, 34, 64, 128, 83, …
$ POPULATION <dbl> 100, 700, 40, 150, 256, 15…
$ population_total <dbl> 1, 269, 1, 0, 139, 0, 20, …
$ population_percent_difference <dbl> -99.00, -61.57, -97.50, -1…
$ household_count <dbl> 0.41121610, 82.16845104, 0…
$ households_income_100k_125k <dbl> 0.052575575, 3.423685460, …
$ households_income_10k_15k <dbl> 0.000000000, 1.943172829, …
$ households_income_125k_150k <dbl> 0.00000000, 0.00000000, 0.…
$ households_income_150k_200k <dbl> 0.000000000, 2.961025263, …
$ households_income_15k_20k <dbl> 0.000000000, 3.423685460, …
$ households_income_20k_25k <dbl> 0.000000000, 6.754838880, …
$ households_income_25k_30k <dbl> 0.0000000000, 11.381440853…
$ households_income_30k_35k <dbl> 0.0000000000, 1.9431728286…
$ households_income_35k_40k <dbl> 0.0694748669, 1.5730446707…
$ households_income_40k_45k <dbl> 0.037553982, 1.387980592, …
$ households_income_45k_50k <dbl> 0.018776991, 3.146089341, …
$ households_income_50k_60k <dbl> 0.046942478, 1.295448552, …
$ households_income_60k_75k <dbl> 0.050697876, 15.452850589,…
$ households_income_75k_100k <dbl> 0.065719469, 19.709324404,…
$ households_income_above_200k <dbl> 0.013143894, 0.000000000, …
$ households_income_below_10k <dbl> 0.056330973, 7.772691314, …
$ households_mortgage_over30pct <dbl> 0.054453274, 3.793813618, …
$ households_mortgage_over50pct <dbl> 0.000000000, 1.573044671, …
$ households_mortgage_total <dbl> 0.054453274, 9.345735985, …
$ households_no_mortgage_over30pct <dbl> 0.056330973, 0.832788355, …
$ households_no_mortgage_over50pct <dbl> 0.0563309732, 0.8327883551…
$ households_no_mortgage_total <dbl> 0.16523752, 24.52099046, 0…
$ households_rent_over30pct <dbl> 0.076985663, 33.866726441,…
$ households_rent_over50pct <dbl> 0.0488201768, 26.649227363…
$ households_rent_total <dbl> 0.191525309, 48.301724596,…
$ population_asian <dbl> 0, 43, 0, 0, 19, 0, 11, 0,…
$ population_black_or_african_american <dbl> 0, 65, 0, 0, 15, 0, 1, 0, …
$ population_hispanic_or_latino <dbl> 1, 52, 0, 0, 84, 0, 3, 0, …
$ population_native_american_or_alaska_native <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ population_other_or_multiple <dbl> 0, 2, 0, 0, 11, 0, 2, 0, 0…
$ population_pacific_islander <dbl> 0, 0, 0, 0, 0, 0, 2, 0, 0,…
$ population_white <dbl> 0, 108, 0, 0, 11, 0, 2, 0,…
$ percent_asian <dbl> 0.00, 15.91, 5.93, 0.00, 1…
$ percent_black_or_african_american <dbl> 0.00, 24.01, 0.00, 3.23, 1…
$ percent_hispanic_or_latino <dbl> 69.19, 19.27, 44.60, 3.90,…
$ percent_native_american_or_alaska_native <dbl> 0.00, 0.00, 0.00, 0.00, 0.…
$ percent_other_or_multiple <dbl> 2.10, 0.62, 3.63, 3.63, 8.…
$ percent_pacific_islander <dbl> 0.00, 0.00, 0.00, 0.00, 0.…
$ percent_white <dbl> 28.71, 40.19, 45.84, 89.23…
$ poverty_rate_percent <dbl> 17.42, 37.48, 15.75, 35.94…
$ poverty_above_count <dbl> 0.96138194, 168.40831181, …
$ poverty_below_count <dbl> 0.20279150, 100.95245505, …
$ poverty_total_assessed <dbl> 1.16417345, 269.36076686, …
$ geometry <MULTIPOLYGON [m]> MULTIPOLYGON …Table 2 provides a complete view of the cleaned and re-formatted dataset. These results are saved locally in tabular and spatial format in Section 6.5.
water_systems_sac_demographics %>%
kable(caption = 'A Caption') %>%
scroll_box(height = "400px")| WATER_SY_1 | WATER_SYST | GLOBALID | BOUNDARY_T | OWNER_TYPE | COUNTY | REGULATING | FEDERAL_CL | STATE_CLAS | SERVICE_CO | POPULATION | population_total | population_percent_difference | household_count | households_income_100k_125k | households_income_10k_15k | households_income_125k_150k | households_income_150k_200k | households_income_15k_20k | households_income_20k_25k | households_income_25k_30k | households_income_30k_35k | households_income_35k_40k | households_income_40k_45k | households_income_45k_50k | households_income_50k_60k | households_income_60k_75k | households_income_75k_100k | households_income_above_200k | households_income_below_10k | households_mortgage_over30pct | households_mortgage_over50pct | households_mortgage_total | households_no_mortgage_over30pct | households_no_mortgage_over50pct | households_no_mortgage_total | households_rent_over30pct | households_rent_over50pct | households_rent_total | population_asian | population_black_or_african_american | population_hispanic_or_latino | population_native_american_or_alaska_native | population_other_or_multiple | population_pacific_islander | population_white | percent_asian | percent_black_or_african_american | percent_hispanic_or_latino | percent_native_american_or_alaska_native | percent_other_or_multiple | percent_pacific_islander | percent_white | poverty_rate_percent | poverty_above_count | poverty_below_count | poverty_total_assessed | geometry |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| HOOD WATER MAINTENCE DIST [SWS] | CA3400101 | {36268DB3-9DB2-4305-A85A-2C3A85F20F34} | Water Service Area | L | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 82 | 100 | 1 | -99.00 | 0.4112161 | 0.0525756 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0694749 | 0.0375540 | 0.0187770 | 0.0469425 | 0.0506979 | 0.0657195 | 0.0131439 | 0.0563310 | 0.0544533 | 0.0000000 | 0.0544533 | 0.0563310 | 0.0563310 | 0.1652375 | 0.0769857 | 0.0488202 | 0.1915253 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0.00 | 0.00 | 69.19 | 0.00 | 2.10 | 0.00 | 28.71 | 17.42 | 0.9613819 | 0.2027915 | 1.1641734 | MULTIPOLYGON (((-132703 403... |
| MC CLELLAN MHP | CA3400179 | {E3BF3C3E-D516-45B2-A5AD-6900F8D16715} | Water Service Area | P | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 199 | 700 | 269 | -61.57 | 82.1684510 | 3.4236855 | 1.9431728 | 0.0000000 | 2.9610253 | 3.4236855 | 6.7548389 | 11.3814409 | 1.9431728 | 1.5730447 | 1.3879806 | 3.1460893 | 1.2954486 | 15.4528506 | 19.7093244 | 0.0000000 | 7.7726913 | 3.7938136 | 1.5730447 | 9.3457360 | 0.8327884 | 0.8327884 | 24.5209905 | 33.8667264 | 26.6492274 | 48.3017246 | 43 | 65 | 52 | 0 | 2 | 0 | 108 | 15.91 | 24.01 | 19.27 | 0.00 | 0.62 | 0.00 | 40.19 | 37.48 | 168.4083118 | 100.9524550 | 269.3607669 | MULTIPOLYGON (((-119809.2 7... |
| MAGNOLIA MUTUAL WATER | CA3400130 | {BADB48CD-AF05-4135-8FD5-9990BE415405} | Water Service Area | P | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 34 | 40 | 1 | -97.50 | 0.3600789 | 0.0024719 | 0.0090638 | 0.0148316 | 0.0140076 | 0.0131837 | 0.0000000 | 0.0205995 | 0.0090638 | 0.0000000 | 0.0230714 | 0.0222474 | 0.0090638 | 0.1384285 | 0.0000000 | 0.0609745 | 0.0230714 | 0.0000000 | 0.0000000 | 0.1235969 | 0.0230714 | 0.0230714 | 0.0494387 | 0.0650943 | 0.0296632 | 0.1870433 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5.93 | 0.00 | 44.60 | 0.00 | 3.63 | 0.00 | 45.84 | 15.75 | 0.7844281 | 0.1466683 | 0.9310964 | MULTIPOLYGON (((-137015.5 3... |
| KORTHS PIRATES LAIR | CA3400135 | {7A556C0E-57E2-499A-9B87-B5707C837DA8} | Water Service Area | P | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 64 | 150 | 0 | -100.00 | 0.0489835 | 0.0062708 | 0.0081639 | 0.0000000 | 0.0014198 | 0.0024847 | 0.0027213 | 0.0018931 | 0.0005916 | 0.0004733 | 0.0022480 | 0.0013015 | 0.0000000 | 0.0067441 | 0.0035495 | 0.0026030 | 0.0085189 | 0.0035495 | 0.0035495 | 0.0084005 | 0.0031946 | 0.0008282 | 0.0301710 | 0.0069807 | 0.0069807 | 0.0104119 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.00 | 3.23 | 3.90 | 0.00 | 3.63 | 0.00 | 89.23 | 35.94 | 0.0563192 | 0.0315908 | 0.0879099 | MULTIPOLYGON (((-137297.1 1... |
| EL DORADO MOBILE HOME PARK | CA3400121 | {23B9391F-B6E7-44CE-A0EA-C2EE522857BF} | Water Service Area | P | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 128 | 256 | 139 | -45.70 | 48.2151375 | 0.0000000 | 9.7199081 | 4.3382641 | 0.0000000 | 0.0000000 | 3.6792873 | 5.6562177 | 0.7688063 | 0.0000000 | 7.5233187 | 1.4826978 | 7.2487450 | 0.4393179 | 0.9335505 | 0.8237210 | 5.6013030 | 0.0000000 | 0.0000000 | 3.1850546 | 5.3267293 | 5.3267293 | 9.6649934 | 17.1883121 | 9.6649934 | 35.3650894 | 19 | 15 | 84 | 0 | 11 | 0 | 11 | 13.27 | 10.48 | 60.26 | 0.00 | 8.19 | 0.00 | 7.80 | 43.12 | 78.8026450 | 59.7472319 | 138.5498768 | MULTIPOLYGON (((-124533.7 5... |
| RIVER'S EDGE MARINA & RESORT | CA3400107 | {F56E068C-AE1B-4C68-A2AF-F5058871E4E2} | Water Service Area | P | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 83 | 150 | 0 | -100.00 | 0.0782305 | 0.0100150 | 0.0130384 | 0.0000000 | 0.0022676 | 0.0039682 | 0.0043461 | 0.0030234 | 0.0009448 | 0.0007559 | 0.0035903 | 0.0020786 | 0.0000000 | 0.0107709 | 0.0056689 | 0.0041572 | 0.0136053 | 0.0056689 | 0.0056689 | 0.0134163 | 0.0051020 | 0.0013227 | 0.0481854 | 0.0111488 | 0.0111488 | 0.0166287 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.00 | 3.23 | 3.90 | 0.00 | 3.63 | 0.00 | 89.23 | 35.94 | 0.0899462 | 0.0504530 | 0.1403991 | MULTIPOLYGON (((-141320.7 1... |
| LAGUNA VILLAGE RV PARK | CA3400397 | {5184D6CC-8084-494D-BD81-B0009F76F1FD} | Water Service Area | P | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 28 | 32 | 20 | -37.50 | 6.6830099 | 0.3588648 | 0.0000000 | 0.8057530 | 0.8531502 | 0.0947945 | 0.2099020 | 0.1963600 | 0.1151076 | 0.0000000 | 0.2708413 | 0.2911544 | 0.1083365 | 0.7041875 | 1.3745198 | 0.6432482 | 0.6567902 | 1.2729543 | 0.4807434 | 2.8302919 | 0.1625048 | 0.0067710 | 1.0562812 | 0.7380426 | 0.3317806 | 2.7964368 | 11 | 1 | 3 | 0 | 2 | 2 | 2 | 52.62 | 7.28 | 12.79 | 0.00 | 10.45 | 8.38 | 8.48 | 11.79 | 17.8890701 | 2.3901748 | 20.2792449 | MULTIPOLYGON (((-122362.2 4... |
| SPINDRIFT MARINA | CA3400169 | {3B1EC48B-B696-452E-A769-99EF03DE955E} | Water Service Area | P | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 50 | 100 | 0 | -100.00 | 0.0954742 | 0.0122225 | 0.0159124 | 0.0000000 | 0.0027674 | 0.0048429 | 0.0053041 | 0.0036898 | 0.0011531 | 0.0009225 | 0.0043817 | 0.0025368 | 0.0000000 | 0.0131450 | 0.0069184 | 0.0050735 | 0.0166042 | 0.0069184 | 0.0069184 | 0.0163736 | 0.0062266 | 0.0016143 | 0.0588066 | 0.0136062 | 0.0136062 | 0.0202940 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.00 | 3.23 | 3.90 | 0.00 | 3.63 | 0.00 | 89.23 | 35.94 | 0.1097723 | 0.0615740 | 0.1713463 | MULTIPOLYGON (((-140144.2 1... |
| SAC CITY MOBILE HOME COMMUNITY LP | CA3400296 | {BBEBB4AF-57F6-4CBA-9746-6EDDFF0BB86B} | Water Service Area | P | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 164 | 350 | 229 | -34.57 | 89.4406295 | 3.7479883 | 16.1844949 | 4.2590776 | 0.0000000 | 9.0292445 | 9.8810600 | 7.6663397 | 0.0000000 | 0.0000000 | 4.2590776 | 2.0443572 | 6.6441611 | 1.3629048 | 13.2883221 | 0.0000000 | 11.0736018 | 1.5332679 | 1.5332679 | 3.5776252 | 1.5332679 | 0.0000000 | 15.3326793 | 40.7167818 | 30.1542694 | 70.5303250 | 123 | 7 | 82 | 0 | 0 | 0 | 17 | 53.57 | 3.27 | 35.66 | 0.00 | 0.00 | 0.00 | 7.50 | 48.14 | 118.9134465 | 110.3952913 | 229.3087378 | MULTIPOLYGON (((-124542.8 5... |
| ORANGE VALE WATER COMPANY | CA3410016 | {E7FB0496-8E98-4C1C-B073-40B7F2308EE3} | Water Service Area | P | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 5684 | 18005 | 17387 | -3.43 | 6595.2837104 | 900.7356837 | 111.3937778 | 625.6077965 | 677.8935478 | 60.8572084 | 93.5577120 | 225.5184953 | 57.9795142 | 273.6232058 | 119.6327762 | 180.8290498 | 371.6355706 | 751.7849002 | 989.6690621 | 765.6465676 | 388.9188424 | 1021.3048996 | 452.6481914 | 3246.4149049 | 314.6881475 | 184.8067075 | 1686.0642367 | 693.1235814 | 304.7507104 | 1662.8045688 | 633 | 241 | 2658 | 181 | 1281 | 86 | 12308 | 3.64 | 1.39 | 15.28 | 1.04 | 7.37 | 0.49 | 70.79 | 11.01 | 15384.0773580 | 1903.9653820 | 17288.0427400 | MULTIPOLYGON (((-104831.8 7... |
| GOLDEN STATE WATER CO. - CORDOVA | CA3410015 | {D09864D9-0038-4D96-AA06-F1D0F4207324} | Water Service Area | P | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 14798 | 44928 | 48115 | 7.09 | 18022.2724131 | 2565.3574304 | 481.6820516 | 1671.0168964 | 1948.4422260 | 309.5110295 | 496.0689166 | 480.3416859 | 437.3376210 | 389.0523221 | 469.4236925 | 597.8111431 | 1276.1636931 | 1691.6766283 | 2652.7212636 | 2046.7106756 | 508.9551374 | 2173.8952361 | 835.7380588 | 7379.6730993 | 364.1246811 | 201.1308054 | 3505.8045695 | 2744.1803381 | 1410.0874409 | 7136.7947443 | 6050 | 3982 | 9009 | 229 | 2615 | 188 | 26042 | 12.57 | 8.28 | 18.72 | 0.48 | 5.43 | 0.39 | 54.13 | 9.21 | 43427.4521762 | 4408.0063561 | 47835.4585323 | MULTIPOLYGON (((-105024.5 6... |
| HOLIDAY MOBILE VILLAGE | CA3400335 | {31419106-A98A-447B-AAF5-674FA6C8D17F} | Water Service Area | P | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 115 | 200 | 46 | -77.00 | 16.1279783 | 0.5855042 | 1.0379392 | 0.4258212 | 0.0000000 | 0.0000000 | 1.1177807 | 0.3459797 | 0.8782562 | 4.6840333 | 0.5588903 | 0.3992074 | 0.0000000 | 2.4218581 | 1.8097401 | 0.0000000 | 1.8629678 | 0.3992074 | 0.0000000 | 2.4750858 | 1.1177807 | 0.7718009 | 1.9960369 | 5.7219725 | 3.8323909 | 11.6568556 | 15 | 3 | 18 | 0 | 3 | 0 | 7 | 32.49 | 7.10 | 38.66 | 0.00 | 6.64 | 0.00 | 15.12 | 22.33 | 35.8222093 | 10.2995505 | 46.1217599 | MULTIPOLYGON (((-123885.3 5... |
| SOUTHWEST TRACT W M D [SWS] | CA3400156 | {0B3A9348-87E2-475F-B9EB-8A0E7658CA58} | Water Service Area | L | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 33 | 150 | 174 | 16.00 | 56.6169348 | 0.0000000 | 2.0808674 | 1.3005421 | 2.0808674 | 6.9362248 | 0.0000000 | 7.1963332 | 0.0000000 | 0.0000000 | 10.2309316 | 11.6181765 | 3.1213012 | 2.4276787 | 4.5952489 | 4.4218433 | 0.6069197 | 1.0404337 | 0.0000000 | 3.2080040 | 0.0000000 | 0.0000000 | 8.3234697 | 28.6986300 | 7.0229276 | 45.0854611 | 75 | 24 | 29 | 3 | 0 | 1 | 42 | 43.11 | 13.69 | 16.58 | 1.55 | 0.00 | 0.60 | 24.48 | 21.83 | 135.6898973 | 37.8891279 | 173.5790252 | MULTIPOLYGON (((-125846.9 5... |
| CARMICHAEL WATER DISTRICT | CA3410004 | {2E757010-FDE6-4A95-9D60-16E621A3CEAC} | Water Service Area | L | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 11704 | 37897 | 39253 | 3.58 | 15937.0599353 | 1724.3160580 | 533.5977949 | 1199.7655014 | 1678.4193378 | 512.5932529 | 471.9441689 | 397.9273383 | 607.1599246 | 521.5633164 | 683.9909627 | 540.5514556 | 996.3263051 | 1594.7356744 | 1782.2476094 | 2121.9683481 | 569.9528868 | 1399.3542055 | 668.5650482 | 5256.1106375 | 358.0036122 | 177.4504952 | 3146.6518891 | 4055.8665545 | 2068.2998480 | 7534.2974087 | 3326 | 2230 | 6192 | 68 | 2116 | 295 | 25026 | 8.47 | 5.68 | 15.78 | 0.17 | 5.39 | 0.75 | 63.76 | 12.92 | 33699.9843977 | 5000.0233004 | 38700.0076981 | MULTIPOLYGON (((-112199 690... |
| SCWA - ARDEN PARK VISTA | CA3410002 | {0FF3353C-7B37-44EC-A4B4-AB9A9D8BF79C} | Water Service Area | L | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 3043 | 10035 | 8086 | -19.42 | 3303.3321668 | 464.8463772 | 36.2252598 | 207.8772002 | 416.0024649 | 48.4429082 | 77.3408834 | 64.6262639 | 37.7691910 | 17.5275827 | 48.6809905 | 161.5983651 | 138.5712947 | 186.9749090 | 253.0854846 | 1065.0769123 | 78.6860792 | 520.3241678 | 111.8599784 | 1822.9354385 | 76.4172240 | 22.9130304 | 673.1932014 | 384.0582803 | 225.3146454 | 807.2035269 | 396 | 270 | 990 | 12 | 395 | 8 | 6016 | 4.90 | 3.33 | 12.24 | 0.15 | 4.88 | 0.10 | 74.40 | 6.51 | 7514.9400515 | 523.1232757 | 8038.0633273 | MULTIPOLYGON (((-120321.1 6... |
| SCWA - LAGUNA/VINEYARD | CA3410029 | {0D088865-F70E-43D5-899E-1B988AB097B9} | Water Service Area | L | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 47411 | 172666 | 145495 | -15.74 | 45136.7375015 | 5322.5294772 | 665.6645745 | 5057.0689734 | 6577.5987020 | 742.3252838 | 877.9535408 | 838.5620300 | 1336.4151580 | 850.4024099 | 788.0623175 | 752.0385993 | 2362.8780777 | 3198.0928072 | 6037.4843391 | 8037.9755292 | 1691.6856820 | 7231.9926132 | 2915.5290405 | 24581.2965388 | 860.9255708 | 470.9663263 | 7878.0360736 | 6368.0895369 | 3336.6734065 | 12677.4048891 | 50411 | 16568 | 27502 | 246 | 10052 | 2220 | 38496 | 34.65 | 11.39 | 18.90 | 0.17 | 6.91 | 1.53 | 26.46 | 10.13 | 130488.6918979 | 14709.7353786 | 145198.4272765 | MULTIPOLYGON (((-125203.9 4... |
| RIO COSUMNES CORRECTIONAL CENTER [SWS] | CA3400229 | {887BC04D-196C-4104-AF6A-DE128C8105D0} | Water Service Area | L | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 13 | 2800 | 22 | -99.21 | 1.2918032 | 0.6297541 | 0.0000000 | 0.2422131 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.3068033 | 0.0000000 | 0.0000000 | 0.0000000 | 0.1130328 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.8719672 | 0.0000000 | 0.0000000 | 0.1130328 | 0.3068033 | 0.0000000 | 0.3068033 | 1 | 4 | 6 | 1 | 2 | 0 | 8 | 4.50 | 16.82 | 25.74 | 2.97 | 10.66 | 1.81 | 37.49 | 0.00 | 4.4567210 | 0.0000000 | 4.4567210 | MULTIPOLYGON (((-124028.8 3... |
| SCWA MATHER-SUNRISE | CA3410704 | {4E3FC0F8-4DB3-4B03-AAEB-C965EE668712} | Water Service Area | L | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 6921 | 22839 | 18249 | -20.10 | 5503.4347766 | 644.6310835 | 34.5914763 | 754.6721725 | 1002.5876562 | 96.5328507 | 57.1333078 | 68.4852856 | 38.5727314 | 11.5056903 | 19.8169793 | 35.6752369 | 188.7925174 | 320.4496478 | 533.0655801 | 1469.0347467 | 227.8878142 | 881.1934264 | 266.4397612 | 3755.5193696 | 60.4771366 | 43.0746683 | 855.3673715 | 318.0997475 | 166.9204055 | 892.5480355 | 4507 | 1553 | 2708 | 23 | 1180 | 164 | 8114 | 24.70 | 8.51 | 14.84 | 0.12 | 6.47 | 0.90 | 44.47 | 5.52 | 17205.7757805 | 1005.1926521 | 18210.9684327 | MULTIPOLYGON (((-108416.1 5... |
| TUNNEL TRAILER PARK | CA3400192 | {896615F1-429B-4829-8D45-45AF249034BC} | Water Service Area | P | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 21 | 44 | 0 | -100.00 | 0.0165565 | 0.0017649 | 0.0000000 | 0.0000000 | 0.0094128 | 0.0000000 | 0.0014287 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0026053 | 0.0000000 | 0.0013447 | 0.0000000 | 0.0000000 | 0.0026894 | 0.0000000 | 0.0076479 | 0.0006723 | 0.0000000 | 0.0056309 | 0.0000000 | 0.0000000 | 0.0032777 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4.65 | 0.00 | 49.74 | 0.00 | 10.67 | 0.00 | 34.94 | 0.00 | 0.0488291 | 0.0000000 | 0.0488291 | MULTIPOLYGON (((-136156.2 2... |
| IMPERIAL MANOR MOBILEHOME COMMUNITY | CA3400190 | {1F6AA119-33B3-4518-92C8-62843A00AE53} | Water Service Area | P | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 186 | 200 | 209 | 4.50 | 124.3703460 | 0.0000000 | 25.9994950 | 0.0000000 | 0.0000000 | 17.5734656 | 2.8363085 | 0.0704618 | 15.5996970 | 7.4411127 | 4.5053287 | 6.1453352 | 0.9454362 | 3.7817447 | 28.8358035 | 6.3816942 | 4.2544628 | 0.0000000 | 0.0000000 | 8.9816437 | 36.8720111 | 34.0357025 | 88.8710010 | 26.5177012 | 22.0123725 | 26.5177012 | 6 | 1 | 52 | 0 | 21 | 0 | 129 | 2.93 | 0.45 | 24.93 | 0.00 | 10.05 | 0.00 | 61.63 | 21.48 | 164.4684875 | 44.9910101 | 209.4594975 | MULTIPOLYGON (((-115157 740... |
| CALAM - ISLETON | CA3410012 | {24CB80A8-8A7C-46E9-8413-3346147E7444} | Water Service Area | P | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 480 | 1581 | 34 | -97.85 | 16.2336094 | 2.9637515 | 1.2133506 | 0.9302355 | 0.4914932 | 0.4448952 | 0.6812770 | 0.7280104 | 0.3235602 | 0.9302355 | 0.7280104 | 0.1617801 | 2.1178443 | 1.1729056 | 0.7577787 | 1.4155757 | 1.1729056 | 3.7400335 | 1.1324606 | 6.1829762 | 1.8551326 | 1.6178008 | 6.5225515 | 0.8089004 | 0.6066753 | 3.5280817 | 2 | 0 | 14 | 0 | 1 | 0 | 17 | 4.55 | 0.00 | 42.06 | 0.00 | 2.25 | 0.00 | 51.14 | 20.89 | 26.6548535 | 7.0374337 | 33.6922872 | MULTIPOLYGON (((-138559.3 1... |
| FOLSOM, CITY OF - ASHLAND | CA3410030 | {C83A0A46-997F-44BF-9441-C2DBC0D706CF} | Water Service Area | L | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 1079 | 3538 | 3845 | 8.68 | 1799.7030393 | 132.3166375 | 17.2265963 | 79.8748540 | 122.5819340 | 103.5596466 | 42.9221364 | 33.7589226 | 209.0008907 | 103.1571051 | 73.6177167 | 43.3463904 | 43.3896373 | 158.0481682 | 247.7733397 | 345.3208615 | 43.8082024 | 163.9652512 | 90.3906967 | 593.7964348 | 367.6575353 | 81.6863124 | 847.4360028 | 195.9003890 | 74.3934966 | 358.4706017 | 125 | 43 | 318 | 1 | 423 | 1 | 2934 | 3.26 | 1.12 | 8.26 | 0.03 | 10.99 | 0.02 | 76.32 | 3.79 | 3636.5160126 | 143.3077376 | 3779.8237502 | MULTIPOLYGON (((-100505 774... |
| LOCKE WATER WORKS CO [SWS] | CA3400138 | {66BAF16E-A801-4313-B7B8-FF4E564CA9F6} | Water Service Area | P | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 44 | 80 | 1 | -98.75 | 0.4115898 | 0.0028256 | 0.0103604 | 0.0169534 | 0.0160115 | 0.0150696 | 0.0000000 | 0.0235463 | 0.0103604 | 0.0000000 | 0.0263719 | 0.0254300 | 0.0103604 | 0.1582313 | 0.0000000 | 0.0696971 | 0.0263719 | 0.0000000 | 0.0000000 | 0.1412780 | 0.0263719 | 0.0263719 | 0.0565112 | 0.0744064 | 0.0339067 | 0.2138006 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5.93 | 0.00 | 44.60 | 0.00 | 3.63 | 0.00 | 45.84 | 15.75 | 0.8966441 | 0.1676498 | 1.0642940 | MULTIPOLYGON (((-131976.7 2... |
| DEL PASO MANOR COUNTY WATER DI | CA3410007 | {BC88D650-D07A-47DF-A872-CD3F6142791A} | Water Service Area | L | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 1796 | 4520 | 5592 | 23.72 | 2221.9390972 | 170.5870031 | 45.3809110 | 120.2825323 | 346.5586719 | 53.7173908 | 66.3032589 | 21.4396308 | 51.3430451 | 66.0596461 | 237.1084233 | 40.3903109 | 157.6807268 | 278.1739919 | 165.5702722 | 231.0331020 | 170.3101801 | 325.8336617 | 188.7334656 | 921.6068532 | 111.5401988 | 67.6595556 | 571.5015208 | 508.8525659 | 114.0279242 | 728.8307232 | 119 | 390 | 687 | 15 | 382 | 31 | 3967 | 2.13 | 6.97 | 12.28 | 0.26 | 6.84 | 0.56 | 70.95 | 11.10 | 4970.7949939 | 620.9055619 | 5591.7005557 | MULTIPOLYGON (((-120274.9 6... |
| EAST WALNUT GROVE [SWS] | CA3400106 | {6E5DA14E-1740-4320-A7E4-FD9B48240017} | Water Service Area | L | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 166 | 300 | 3 | -99.00 | 1.3439937 | 0.0092265 | 0.0338305 | 0.0553590 | 0.0522835 | 0.0492080 | 0.0000000 | 0.0768875 | 0.0338305 | 0.0000000 | 0.0861140 | 0.0830385 | 0.0338305 | 0.5166841 | 0.0000000 | 0.2275870 | 0.0861140 | 0.0000000 | 0.0000000 | 0.4613251 | 0.0861140 | 0.0861140 | 0.1845300 | 0.2429645 | 0.1107180 | 0.6981386 | 0 | 0 | 2 | 0 | 0 | 0 | 2 | 5.93 | 0.00 | 44.60 | 0.00 | 3.63 | 0.00 | 45.84 | 15.75 | 2.9278765 | 0.5474391 | 3.4753156 | MULTIPOLYGON (((-132506.6 2... |
| FOLSOM STATE PRISON | CA3410032 | {CFEFFCD1-76A3-4403-A906-D04B910D61C5} | Water Service Area | S | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 2790 | 9703 | 3536 | -63.56 | 22.5426613 | 4.1517526 | 0.0000000 | 4.0329731 | 12.1469937 | 0.1064474 | 0.0085195 | 0.0001474 | 0.0049313 | 0.0005898 | 0.0000000 | 0.0078093 | 0.2240872 | 0.1126992 | 0.2620161 | 1.3895998 | 0.0940949 | 0.9386397 | 0.1192110 | 3.1453984 | 0.1115472 | 0.0004353 | 0.4520514 | 0.0036714 | 0.0002949 | 18.9452115 | 70 | 1390 | 1257 | 57 | 77 | 34 | 652 | 1.97 | 39.31 | 35.55 | 1.60 | 2.17 | 0.96 | 18.43 | 2.20 | 28.1916388 | 0.6348719 | 28.8265107 | MULTIPOLYGON (((-99838.11 7... |
| CALAM - ARDEN | CA3410045 | {FC35B709-C658-4890-B64C-374368BFAC79} | Water Service Area | P | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 1185 | 3908 | 10112 | 158.75 | 3823.4030385 | 227.8237249 | 258.6358544 | 148.0773037 | 62.2606019 | 239.2704507 | 167.2117959 | 318.9373630 | 189.7774321 | 141.9916915 | 236.0923455 | 206.5761768 | 440.1718547 | 393.6199161 | 534.5491374 | 57.5818264 | 200.8255635 | 84.3191682 | 45.9095962 | 264.6683172 | 8.0957445 | 3.1275988 | 132.9715104 | 2124.1154199 | 1169.6691535 | 3425.7632110 | 1082 | 1977 | 3433 | 70 | 1100 | 59 | 2392 | 10.70 | 19.55 | 33.95 | 0.69 | 10.87 | 0.58 | 23.65 | 31.19 | 6903.8350964 | 3129.7143646 | 10033.5494610 | MULTIPOLYGON (((-123013.9 6... |
| EDGEWATER MOBILE HOME PARK | CA3400433 | {FA333DDB-BCD6-4D29-9D40-664577E929C9} | Water Service Area | P | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 22 | 40 | 0 | -100.00 | 0.0323964 | 0.0041474 | 0.0053994 | 0.0000000 | 0.0009390 | 0.0016433 | 0.0017998 | 0.0012520 | 0.0003913 | 0.0003130 | 0.0014868 | 0.0008608 | 0.0000000 | 0.0044604 | 0.0023476 | 0.0017215 | 0.0056342 | 0.0023476 | 0.0023476 | 0.0055559 | 0.0021128 | 0.0005478 | 0.0199543 | 0.0046169 | 0.0046169 | 0.0068862 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.00 | 3.23 | 3.90 | 0.00 | 3.63 | 0.00 | 89.23 | 35.94 | 0.0372481 | 0.0208933 | 0.0581414 | MULTIPOLYGON (((-153715.8 7... |
| CALAM - LINCOLN OAKS | CA3410013 | {488DCE2C-6EEA-4377-8984-5833F5D03CDF} | Water Service Area | P | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 14390 | 47487 | 42916 | -9.63 | 15621.3809893 | 1888.7872953 | 375.3828366 | 1272.1217031 | 1555.1540910 | 308.1458139 | 622.2810854 | 488.2818455 | 616.3732436 | 585.1829345 | 628.7492052 | 645.4504947 | 1034.6867979 | 1640.6899168 | 2442.1704417 | 778.1108146 | 739.8124694 | 2670.6595161 | 919.4308898 | 7390.0598679 | 502.5707184 | 297.5609445 | 3331.7506863 | 2522.8453244 | 1302.1867347 | 4899.5704351 | 2706 | 1486 | 9056 | 143 | 2708 | 288 | 26529 | 6.31 | 3.46 | 21.10 | 0.33 | 6.31 | 0.67 | 61.82 | 9.51 | 38749.4769263 | 4073.9481050 | 42823.4250313 | MULTIPOLYGON (((-113737.5 7... |
| VIEIRA'S RESORT, INC | CA3400164 | {C8E1FF09-C1ED-47D9-BD1C-AE3C754ABA72} | Water Service Area | P | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 107 | 150 | 4 | -97.33 | 2.1367242 | 0.3992300 | 0.1686888 | 0.1293280 | 0.0000000 | 0.0618525 | 0.0843444 | 0.1012133 | 0.0449837 | 0.1293280 | 0.1012133 | 0.0224918 | 0.2755250 | 0.1630658 | 0.0955903 | 0.1968035 | 0.1630658 | 0.5004433 | 0.1574428 | 0.8040830 | 0.2530331 | 0.2249183 | 0.8659356 | 0.1124592 | 0.0843444 | 0.4667055 | 0 | 0 | 2 | 0 | 0 | 0 | 2 | 4.55 | 0.00 | 41.43 | 0.00 | 1.56 | 0.00 | 52.47 | 22.60 | 3.3512832 | 0.9783948 | 4.3296779 | MULTIPOLYGON (((-143708.1 1... |
| FLORIN COUNTY WATER DISTRICT | CA3410033 | {7D72D48E-DB2F-4E79-B396-70EEB030D102} | Water Service Area | L | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 2323 | 7831 | 9951 | 27.07 | 2755.4087157 | 296.9354278 | 124.6257543 | 214.6215268 | 142.8389081 | 53.3547129 | 154.3761991 | 102.8638213 | 46.0111239 | 85.7880801 | 175.8361672 | 224.2674437 | 257.8035909 | 222.7155372 | 432.0027291 | 137.0102004 | 84.3574930 | 425.6666406 | 89.9994530 | 980.7724658 | 48.5268687 | 27.7142782 | 674.7293649 | 475.7493760 | 259.6899077 | 1099.9068850 | 2743 | 1394 | 2963 | 7 | 430 | 866 | 1548 | 27.56 | 14.01 | 29.78 | 0.07 | 4.32 | 8.70 | 15.56 | 13.06 | 8549.7113979 | 1284.8842784 | 9834.5956763 | MULTIPOLYGON (((-119638.3 5... |
| WESTERNER MOBILE HOME PARK | CA3400331 | {5DF48C4F-72B2-40C9-B2F7-10F9FF855DCC} | Water Service Area | P | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 49 | 65 | 32 | -50.77 | 9.8864417 | 1.7859379 | 0.0000000 | 0.1093431 | 0.6925065 | 0.1093431 | 0.1093431 | 0.6651707 | 0.1366789 | 0.4373725 | 0.6104992 | 0.0000000 | 2.1504150 | 0.9111928 | 0.9476405 | 0.1731266 | 1.0478717 | 1.8679452 | 0.8565212 | 3.9090170 | 0.3280294 | 0.3280294 | 0.8565212 | 3.4260849 | 1.7312663 | 5.1209035 | 10 | 9 | 6 | 0 | 1 | 0 | 6 | 31.36 | 28.31 | 17.59 | 0.55 | 4.57 | 0.00 | 17.62 | 23.76 | 23.8276913 | 7.4262212 | 31.2539125 | MULTIPOLYGON (((-122693.1 4... |
| EL DORADO WEST MHP | CA3400122 | {2667CD45-9C2C-41B7-A95B-2F343B570215} | Water Service Area | P | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 128 | 172 | 148 | -13.95 | 51.1665127 | 0.0000000 | 10.3148892 | 4.6038206 | 0.0000000 | 0.0000000 | 3.9045061 | 6.0024497 | 0.8158669 | 0.0000000 | 7.9838408 | 1.5734577 | 7.6924598 | 0.4662097 | 0.9906956 | 0.8741432 | 5.9441735 | 0.0000000 | 0.0000000 | 3.3800202 | 5.6527924 | 5.6527924 | 10.2566130 | 18.2404539 | 10.2566130 | 37.5298795 | 20 | 16 | 89 | 0 | 12 | 0 | 12 | 13.27 | 10.48 | 60.26 | 0.00 | 8.19 | 0.00 | 7.80 | 43.12 | 83.6263619 | 63.4045169 | 147.0308788 | MULTIPOLYGON (((-124742.1 5... |
| TOKAY PARK WATER CO | CA3400172 | {D2908483-C728-45ED-9A75-BA27D6E66685} | Water Service Area | P | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 198 | 525 | 652 | 24.19 | 172.9103108 | 13.8638644 | 1.8971726 | 3.7737854 | 10.4172265 | 2.5212446 | 20.5379761 | 0.0041485 | 0.0064015 | 12.6022626 | 13.3274782 | 10.3961842 | 17.9662119 | 27.3715141 | 36.0110885 | 0.0027623 | 2.2109893 | 37.8735331 | 11.3805327 | 80.9724226 | 0.0051347 | 0.0011499 | 43.7650747 | 32.2708872 | 12.0942179 | 48.1728135 | 239 | 37 | 214 | 0 | 28 | 0 | 134 | 36.69 | 5.61 | 32.80 | 0.00 | 4.35 | 0.00 | 20.55 | 17.29 | 538.8538080 | 112.6465489 | 651.5003569 | MULTIPOLYGON (((-122789.8 5... |
| LAGUNA DEL SOL INC | CA3400181 | {F64FE571-B7DD-4F78-BBCF-2057DA131C24} | Water Service Area | P | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 112 | 470 | 24 | -94.89 | 8.9804190 | 0.3985393 | 0.8767865 | 0.3985393 | 0.7439400 | 0.9033558 | 0.4251086 | 0.3985393 | 0.0000000 | 0.2656929 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 1.9926965 | 2.4178051 | 0.1594157 | 1.7004344 | 1.7004344 | 4.8621795 | 0.0000000 | 0.0000000 | 2.5240823 | 0.3985393 | 0.3985393 | 1.5941572 | 0 | 0 | 5 | 0 | 0 | 0 | 18 | 1.46 | 0.00 | 21.55 | 0.67 | 1.12 | 0.00 | 75.20 | 6.40 | 22.1587853 | 1.5144494 | 23.6732346 | MULTIPOLYGON (((-105025.5 4... |
| OLYMPIA MOBILODGE | CA3410022 | {B1E1B46F-9300-473E-B970-1D739E004B14} | Water Service Area | P | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 200 | 450 | 290 | -35.56 | 114.4237404 | 2.6713714 | 0.2226143 | 11.7985569 | 5.1201285 | 6.4558141 | 10.0176426 | 8.9045712 | 3.3392142 | 13.1342426 | 0.0000000 | 0.0000000 | 10.0176426 | 18.6995996 | 7.5688856 | 5.3427427 | 11.1307140 | 21.5935853 | 9.7950284 | 30.7207708 | 12.2437855 | 10.0176426 | 50.7560561 | 8.9045712 | 7.3462713 | 32.9469136 | 101 | 18 | 70 | 0 | 3 | 16 | 81 | 34.95 | 6.30 | 24.12 | 0.00 | 1.08 | 5.53 | 28.03 | 23.43 | 221.9464382 | 67.8973557 | 289.8437939 | MULTIPOLYGON (((-123549.9 5... |
| GOLDEN STATE WATER CO - ARDEN WATER SERV | CA3410003 | {E2997C83-F848-48AF-8915-B45E6469AC6E} | Water Service Area | P | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 1716 | 5125 | 6556 | 27.92 | 2173.3862508 | 131.7147921 | 82.2403920 | 171.7079195 | 140.8479724 | 19.2857784 | 140.6953734 | 52.8057022 | 172.6873796 | 34.4563047 | 178.9267558 | 37.1685200 | 138.6454528 | 351.1414354 | 319.0998779 | 182.5319559 | 19.4306387 | 238.5720694 | 123.1865478 | 727.7085408 | 0.2417045 | 0.2417045 | 130.6029265 | 599.3176233 | 334.9873262 | 1315.0747835 | 888 | 322 | 1706 | 0 | 742 | 11 | 2887 | 13.54 | 4.91 | 26.02 | 0.00 | 11.32 | 0.16 | 44.04 | 25.19 | 4827.6115519 | 1625.5697248 | 6453.1812768 | MULTIPOLYGON (((-120307.3 6... |
| ELEVEN OAKS MOBILE HOME COMMUNITY | CA3400191 | {AB99BC88-8951-414B-BA24-E873AF46C168} | Water Service Area | P | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 136 | 262 | 233 | -11.07 | 71.2146352 | 2.9672765 | 1.6841299 | 0.0000000 | 2.5662932 | 2.9672765 | 5.8543563 | 9.8641893 | 1.6841299 | 1.3633432 | 1.2029499 | 2.7266865 | 1.1227533 | 13.3928424 | 17.0818888 | 0.0000000 | 6.7365195 | 3.2880631 | 1.3633432 | 8.0998628 | 0.7217700 | 0.7217700 | 21.2521152 | 29.3519780 | 23.0966384 | 41.8626572 | 37 | 56 | 45 | 0 | 1 | 0 | 94 | 15.91 | 24.01 | 19.27 | 0.00 | 0.62 | 0.00 | 40.19 | 37.48 | 145.9579234 | 87.4945574 | 233.4524808 | MULTIPOLYGON (((-119816 721... |
| CALAM - ANTELOPE | CA3410031 | {AE6C5C3E-859C-4CD7-B3DF-5913A953F8F9} | Water Service Area | P | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 10528 | 34720 | 33120 | -4.61 | 10529.3295307 | 1500.7495311 | 184.1450913 | 1077.3930203 | 1157.7794652 | 100.9615090 | 122.3726286 | 116.4469685 | 468.5101697 | 247.5986494 | 368.3586872 | 449.4701379 | 737.4305999 | 1076.7061396 | 1668.6530780 | 937.3259723 | 315.4278826 | 1860.8089818 | 620.5036265 | 5544.0872962 | 184.3698474 | 106.3231597 | 1747.2662153 | 1678.4584065 | 649.2056543 | 3237.9760191 | 2947 | 3199 | 5245 | 113 | 2082 | 77 | 19456 | 8.90 | 9.66 | 15.84 | 0.34 | 6.29 | 0.23 | 58.74 | 10.26 | 29644.8479042 | 3388.7925699 | 33033.6404741 | MULTIPOLYGON (((-123334.7 8... |
| CALIFORNIA STATE FAIR | CA3410026 | {C97F5649-348B-4378-9C32-3B6D3E82725B} | Water Service Area | S | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 269 | 650 | 532 | -18.15 | 285.4716540 | 20.7008685 | 13.0215140 | 11.0182042 | 17.0281337 | 8.3471244 | 5.3421596 | 9.3487793 | 13.6892840 | 2.0033099 | 0.0000000 | 23.3719483 | 28.7141079 | 30.0496478 | 34.7240374 | 3.3388498 | 64.7736852 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 177.2929219 | 95.4911030 | 285.4716540 | 48 | 91 | 78 | 0 | 52 | 0 | 262 | 9.10 | 17.13 | 14.68 | 0.00 | 9.85 | 0.00 | 49.25 | 28.89 | 373.9511725 | 151.9176638 | 525.8688363 | MULTIPOLYGON (((-125658 651... |
| PLANTATION MOBILE HOME PARK | CA3400401 | {6BF6AFC4-25D1-4131-BA1A-CE81F4561902} | Water Service Area | P | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 44 | 44 | 10 | -77.27 | 3.3251292 | 0.1207143 | 0.2139935 | 0.0877922 | 0.0000000 | 0.0000000 | 0.2304545 | 0.0713312 | 0.1810714 | 0.9657141 | 0.1152273 | 0.0823052 | 0.0000000 | 0.4993181 | 0.3731168 | 0.0000000 | 0.3840908 | 0.0823052 | 0.0000000 | 0.5102921 | 0.2304545 | 0.1591233 | 0.4115259 | 1.1797076 | 0.7901297 | 2.4033112 | 3 | 1 | 4 | 0 | 1 | 0 | 1 | 32.49 | 7.10 | 38.66 | 0.00 | 6.64 | 0.00 | 15.12 | 22.33 | 7.3855181 | 2.1234736 | 9.5089917 | MULTIPOLYGON (((-124338.4 5... |
| CALAM - PARKWAY | CA3410017 | {78A7BD82-DB23-4AC1-8736-574511C4CC84} | Water Service Area | P | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 14779 | 48738 | 58635 | 20.31 | 17666.7886640 | 1633.5316867 | 753.3380936 | 1531.9788719 | 1545.6177541 | 514.1300586 | 712.7247277 | 694.3526534 | 639.9551980 | 713.2865314 | 700.4012000 | 727.1387473 | 1145.1392905 | 1918.3765008 | 2490.3823970 | 865.4412869 | 1080.9936661 | 2718.9202130 | 1049.2628405 | 7162.9956077 | 646.5433097 | 383.4428179 | 3418.0266877 | 3517.2627210 | 1917.1472603 | 7085.7663686 | 19228 | 6965 | 18665 | 21 | 3449 | 1386 | 8921 | 32.79 | 11.88 | 31.83 | 0.04 | 5.88 | 2.36 | 15.21 | 16.78 | 48630.3167965 | 9803.7863660 | 58434.1031625 | MULTIPOLYGON (((-123335.4 5... |
| CITRUS HEIGHTS WATER DISTRICT | CA3410006 | {20EF35DA-A434-4A91-BE3D-7D996B8C6730} | Water Service Area | L | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 19940 | 65911 | 68912 | 4.55 | 25632.8367034 | 2743.5767440 | 568.7994440 | 2331.8277629 | 2533.3929840 | 445.8866083 | 769.4115233 | 665.4069464 | 867.0779805 | 841.0558325 | 723.3234592 | 1164.6015213 | 1875.0627674 | 3057.1645880 | 3953.9411189 | 2080.4093464 | 1011.8980764 | 3552.5017711 | 1380.0948194 | 10344.1670582 | 554.2501347 | 285.7944238 | 4292.6223834 | 5758.6426763 | 2620.3849095 | 10996.0472617 | 2875 | 2092 | 12380 | 162 | 3186 | 71 | 48148 | 4.17 | 3.04 | 17.96 | 0.23 | 4.62 | 0.10 | 69.87 | 10.15 | 61620.0399314 | 6961.1604963 | 68581.2004277 | MULTIPOLYGON (((-113645.4 7... |
| SEQUOIA WATER ASSOC | CA3400155 | {D4629F0D-8EAD-4402-88C8-D55C4121EA5B} | Water Service Area | P | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 18 | 54 | 0 | -100.00 | 0.0948332 | 0.0006510 | 0.0023871 | 0.0039062 | 0.0036892 | 0.0034722 | 0.0000000 | 0.0054252 | 0.0023871 | 0.0000000 | 0.0060763 | 0.0058593 | 0.0023871 | 0.0364576 | 0.0000000 | 0.0160587 | 0.0060763 | 0.0000000 | 0.0000000 | 0.0325514 | 0.0060763 | 0.0060763 | 0.0130206 | 0.0171438 | 0.0078123 | 0.0492612 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5.93 | 0.00 | 44.60 | 0.00 | 3.63 | 0.00 | 45.84 | 15.75 | 0.2065932 | 0.0386277 | 0.2452209 | MULTIPOLYGON (((-136917.7 3... |
| FAIR OAKS WATER DISTRICT | CA3410009 | {797F9D1A-2D43-4FA9-882A-DAA2A539318A} | Water Service Area | L | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 14293 | 35114 | 36003 | 2.53 | 14233.4070807 | 1447.1879511 | 332.3588693 | 1568.3188854 | 1874.6761780 | 113.0360901 | 229.1722624 | 208.1588999 | 391.4233194 | 205.9499753 | 468.9849244 | 293.0716923 | 803.5523705 | 1064.3255362 | 2213.6241800 | 2473.8514603 | 545.7144862 | 1871.7864644 | 844.6722781 | 7090.3860908 | 261.1826011 | 108.3058016 | 3092.2584650 | 1843.9238233 | 768.3349827 | 4050.7625248 | 1372 | 708 | 4655 | 94 | 2113 | 12 | 27050 | 3.81 | 1.97 | 12.93 | 0.26 | 5.87 | 0.03 | 75.13 | 7.97 | 32923.4030065 | 2851.6924166 | 35775.0954231 | MULTIPOLYGON (((-107763.5 6... |
| RANCHO MARINA | CA3400149 | {A2E2869F-FE8D-48EF-8762-16238D0B3F9A} | Water Service Area | P | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 77 | 250 | 0 | -100.00 | 0.1521691 | 0.0194806 | 0.0253615 | 0.0000000 | 0.0044107 | 0.0077187 | 0.0084538 | 0.0058809 | 0.0018378 | 0.0014702 | 0.0069836 | 0.0040431 | 0.0000000 | 0.0209508 | 0.0110267 | 0.0080863 | 0.0264642 | 0.0110267 | 0.0110267 | 0.0260966 | 0.0099241 | 0.0025729 | 0.0937274 | 0.0216859 | 0.0216859 | 0.0323451 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.00 | 3.23 | 3.90 | 0.00 | 3.63 | 0.00 | 89.23 | 35.94 | 0.1749577 | 0.0981381 | 0.2730958 | MULTIPOLYGON (((-138162.3 1... |
| FREEPORT MARINA | CA3400125 | {1A2ECBCF-8086-4599-91D1-7BDC9F543BC8} | Water Service Area | P | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 27 | 42 | 3 | -92.86 | 1.0728029 | 0.1371620 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.1812498 | 0.0979729 | 0.0489864 | 0.1224661 | 0.1322634 | 0.1714525 | 0.0342905 | 0.1469593 | 0.1420607 | 0.0000000 | 0.1420607 | 0.1469593 | 0.1469593 | 0.4310806 | 0.2008444 | 0.1273647 | 0.4996616 | 0 | 0 | 2 | 0 | 0 | 0 | 1 | 0.00 | 0.00 | 69.19 | 0.00 | 2.10 | 0.00 | 28.71 | 17.42 | 2.5081055 | 0.5290535 | 3.0371590 | MULTIPOLYGON (((-130871.1 5... |
| HAPPY HARBOR (SWS) | CA3400128 | {95E0CED3-1431-4EF7-B516-D9DE2E6F03C4} | Water Service Area | P | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 45 | 60 | 0 | -100.00 | 0.0216151 | 0.0027671 | 0.0036025 | 0.0000000 | 0.0006265 | 0.0010964 | 0.0012008 | 0.0008354 | 0.0002611 | 0.0002088 | 0.0009920 | 0.0005743 | 0.0000000 | 0.0029760 | 0.0015663 | 0.0011486 | 0.0037591 | 0.0015663 | 0.0015663 | 0.0037069 | 0.0014097 | 0.0003655 | 0.0133136 | 0.0030804 | 0.0030804 | 0.0045945 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.00 | 3.23 | 3.90 | 0.00 | 3.63 | 0.00 | 89.23 | 35.94 | 0.0248521 | 0.0139402 | 0.0387923 | MULTIPOLYGON (((-139870.3 1... |
| FOLSOM, CITY OF - MAIN | CA3410014 | {6E4D1E97-03EB-48F4-9F64-E74E49CC4CC2} | Water Service Area | L | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 21424 | 68122 | 62462 | -8.31 | 22409.3823384 | 2382.4430031 | 218.1355460 | 1746.9283429 | 4083.1483087 | 390.0036513 | 476.8583096 | 417.5347017 | 283.0639620 | 329.3286579 | 373.3616549 | 451.4793379 | 670.3668140 | 1180.5443541 | 2255.4207366 | 6343.7663048 | 806.9986528 | 2727.7080877 | 1178.6318783 | 11490.6508104 | 237.1898408 | 146.3657554 | 3590.2477618 | 3009.7032962 | 1320.8580528 | 7328.4837662 | 12934 | 1693 | 8433 | 105 | 3897 | 177 | 35222 | 20.71 | 2.71 | 13.50 | 0.17 | 6.24 | 0.28 | 56.39 | 5.48 | 58709.8582238 | 3405.2750719 | 62115.1332958 | MULTIPOLYGON (((-107067.9 6... |
| RANCHO MURIETA COMMUNITY SERVI | CA3410005 | {2C72EE8A-A081-49FB-8EEA-808BF54CDB58} | Water Service Area | L | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 2726 | 5744 | 3239 | -43.61 | 1402.0967904 | 118.3930766 | 42.3473557 | 204.1395173 | 240.7495319 | 0.0000000 | 5.9102352 | 5.3262089 | 18.2285332 | 73.7524494 | 27.3517589 | 74.6522384 | 44.1562938 | 81.2814649 | 87.8793345 | 318.6065856 | 59.3222060 | 205.1382026 | 103.3669845 | 1029.1069175 | 63.0813034 | 57.1710682 | 269.5909676 | 40.5131013 | 39.9972505 | 103.3989053 | 188 | 120 | 661 | 7 | 106 | 0 | 2157 | 5.80 | 3.71 | 20.42 | 0.21 | 3.26 | 0.00 | 66.59 | 6.13 | 3039.9096847 | 198.6877215 | 3238.5974062 | MULTIPOLYGON (((-95334.2 52... |
| CAL AM FRUITRIDGE VISTA | CA3410023 | {501904B9-FD46-4D56-89DE-D87AD8F5FD70} | Water Service Area | P | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 4667 | 15385 | 22603 | 46.92 | 6899.9140996 | 458.8445334 | 339.2099413 | 235.1486797 | 287.1473267 | 520.9996164 | 262.7374008 | 367.1883343 | 301.6105218 | 359.1749480 | 354.9332453 | 565.0609345 | 692.3060889 | 876.2377458 | 783.8075573 | 141.4321445 | 354.0750809 | 745.4935970 | 344.8495494 | 1619.8296502 | 94.9132221 | 57.5416225 | 1236.1776014 | 2130.6340062 | 1058.7419859 | 4043.9068480 | 4075 | 2663 | 10939 | 121 | 1060 | 240 | 3504 | 18.03 | 11.78 | 48.40 | 0.54 | 4.69 | 1.06 | 15.50 | 26.64 | 16546.1806283 | 6009.7140860 | 22555.8947143 | MULTIPOLYGON (((-125839.3 5... |
| B & W RESORT MARINA | CA3400103 | {CEC00BE2-7FBA-4B81-BE20-7D326EB6EBF6} | Water Service Area | P | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 37 | 100 | 0 | -100.00 | 0.0422931 | 0.0079021 | 0.0033389 | 0.0025598 | 0.0000000 | 0.0012243 | 0.0016695 | 0.0020034 | 0.0008904 | 0.0025598 | 0.0020034 | 0.0004452 | 0.0054536 | 0.0032276 | 0.0018921 | 0.0038954 | 0.0032276 | 0.0099055 | 0.0031163 | 0.0159156 | 0.0050084 | 0.0044519 | 0.0171398 | 0.0022260 | 0.0016695 | 0.0092377 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4.55 | 0.00 | 41.43 | 0.00 | 1.56 | 0.00 | 52.47 | 22.60 | 0.0663334 | 0.0193658 | 0.0856992 | MULTIPOLYGON (((-138335.4 1... |
| CALAM - SUBURBAN ROSEMONT | CA3410010 | {C7B2A772-B03D-4109-86B0-996D6A191F5F} | Water Service Area | P | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 16238 | 53563 | 57897 | 8.09 | 21045.1018353 | 2594.5943353 | 612.2587703 | 1593.9305166 | 1670.7351745 | 472.4194316 | 743.9075600 | 652.7686692 | 567.7706604 | 581.9992968 | 873.7032718 | 628.3855992 | 1288.6020796 | 2508.0974882 | 3438.3892872 | 1661.0922839 | 1156.4474108 | 2261.5294332 | 730.4776261 | 8262.3220973 | 438.6402189 | 271.0385123 | 3424.8520826 | 4520.7820567 | 2319.7944294 | 9357.9276554 | 6905 | 7725 | 13791 | 91 | 3942 | 380 | 25062 | 11.93 | 13.34 | 23.82 | 0.16 | 6.81 | 0.66 | 43.29 | 14.52 | 49286.9627735 | 8374.3720099 | 57661.3347833 | MULTIPOLYGON (((-120054.6 5... |
| SAN JUAN WATER DISTRICT | CA3410021 | {5F6CA8E6-CCFE-44C8-978B-6012BDE0AD70} | Water Service Area | L | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 10672 | 29641 | 30122 | 1.62 | 10750.1495842 | 853.9948037 | 168.3039321 | 876.1496099 | 1032.1228618 | 100.4248825 | 274.9765606 | 127.7463448 | 159.7577616 | 110.8699984 | 133.2796406 | 126.6536145 | 472.3792109 | 684.0889993 | 984.1402476 | 4256.4919483 | 388.7691676 | 1754.1276808 | 723.8699666 | 6209.8360395 | 527.7347496 | 356.6210547 | 2882.6803563 | 726.1057528 | 339.1581882 | 1657.6331884 | 2762 | 831 | 3409 | 287 | 1467 | 17 | 21349 | 9.17 | 2.76 | 11.32 | 0.95 | 4.87 | 0.06 | 70.87 | 5.72 | 28296.8723978 | 1717.5297944 | 30014.4021922 | MULTIPOLYGON (((-100141.1 8... |
| ELK GROVE WATER SERVICE | CA3410008 | {E33EFAB9-03BB-4AA6-B34C-E1B2B887AC40} | Water Service Area | P | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 12882 | 42540 | 42647 | 0.25 | 13238.9100963 | 1469.7831520 | 202.1735678 | 1385.9378896 | 1906.7785552 | 252.8846375 | 223.5107451 | 327.6343327 | 101.5057735 | 344.9642849 | 292.3949973 | 244.6466708 | 667.2277312 | 1116.8947162 | 1440.6413362 | 2832.0848266 | 429.8468798 | 1902.8226201 | 627.9585906 | 7551.9501142 | 282.8809066 | 112.6581718 | 2860.9103294 | 1594.5949046 | 863.8740389 | 2826.0496528 | 8939 | 3209 | 7656 | 70 | 2835 | 388 | 19550 | 20.96 | 7.53 | 17.95 | 0.16 | 6.65 | 0.91 | 45.84 | 7.72 | 38993.5138540 | 3264.0175810 | 42257.5314350 | MULTIPOLYGON (((-113074.8 4... |
| DELTA CROSSING MHP | CA3400150 | {178C1128-CAD8-4118-8692-500FC1384637} | Water Service Area | P | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 22 | 30 | 0 | -100.00 | 0.0565275 | 0.0072273 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0095503 | 0.0051623 | 0.0025812 | 0.0064529 | 0.0069691 | 0.0090341 | 0.0018068 | 0.0077435 | 0.0074854 | 0.0000000 | 0.0074854 | 0.0077435 | 0.0077435 | 0.0227142 | 0.0105828 | 0.0067110 | 0.0263279 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.00 | 0.00 | 69.19 | 0.00 | 2.10 | 0.00 | 28.71 | 17.42 | 0.1321555 | 0.0278766 | 0.1600321 | MULTIPOLYGON (((-132728.4 4... |
| GALT, CITY OF | CA3410011 | {1EB5C880-698B-463D-916D-27EB4115BE84} | Water Service Area | L | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 7471 | 26536 | 21490 | -19.02 | 6987.5357777 | 1096.3084285 | 168.1832539 | 504.4991186 | 788.6043595 | 243.2690568 | 210.1360339 | 140.5426889 | 341.9418512 | 160.7063475 | 347.3758689 | 152.2076332 | 550.4693882 | 686.5717245 | 807.1560200 | 650.1046848 | 139.4593192 | 907.4138072 | 523.4602820 | 3724.2608083 | 108.8264389 | 44.2709236 | 1454.3735494 | 906.4809387 | 414.1512468 | 1808.9014201 | 872 | 520 | 9314 | 22 | 789 | 20 | 9952 | 4.06 | 2.42 | 43.34 | 0.10 | 3.67 | 0.09 | 46.31 | 6.58 | 19937.1926609 | 1404.1088571 | 21341.3015180 | MULTIPOLYGON (((-115624.6 2... |
| LINCOLN CHAN-HOME RANCH | CA3400137 | {8E59F56D-9D04-48D4-B6E4-F0584DA465D8} | Water Service Area | P | SACRAMENTO | LPA64 - SACRAMENTO COUNTY | COMMUNITY | COMMUNITY | 19 | 33 | 4 | -87.88 | 1.5038756 | 0.0103241 | 0.0378550 | 0.0619445 | 0.0585032 | 0.0550618 | 0.0000000 | 0.0860341 | 0.0378550 | 0.0000000 | 0.0963582 | 0.0929168 | 0.0378550 | 0.5781490 | 0.0000000 | 0.2546609 | 0.0963582 | 0.0000000 | 0.0000000 | 0.5162044 | 0.0963582 | 0.0963582 | 0.2064818 | 0.2718677 | 0.1238891 | 0.7811894 | 0 | 0 | 2 | 0 | 0 | 0 | 2 | 5.93 | 0.00 | 44.60 | 0.00 | 3.63 | 0.00 | 45.84 | 15.75 | 3.2761774 | 0.6125626 | 3.8887400 | MULTIPOLYGON (((-137040.3 3... |
| RIO LINDA/ELVERTA COMMUNITY WATER DIST | CA3410018 | {03B163CA-7B3B-4A23-8E31-77AF963C2BF3} | Water Service Area | L | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 4621 | 14381 | 11831 | -17.73 | 3761.6033554 | 491.6445232 | 156.4692332 | 430.8331149 | 415.6196965 | 66.8600922 | 168.7735897 | 56.2689625 | 112.6547781 | 116.4832566 | 114.0660929 | 118.0282656 | 173.1294602 | 297.2131196 | 606.9213690 | 259.3021303 | 177.3356710 | 572.5923869 | 157.1920756 | 1918.2923459 | 114.4708846 | 47.4533275 | 772.9578664 | 519.2560180 | 340.4159673 | 1070.3531430 | 765 | 337 | 2585 | 17 | 512 | 21 | 7595 | 6.46 | 2.85 | 21.85 | 0.14 | 4.33 | 0.18 | 64.19 | 13.69 | 10210.2384197 | 1618.8821031 | 11829.1205228 | MULTIPOLYGON (((-125758.1 7... |
| SACRAMENTO SUBURBAN WATER DISTRICT | CA3410001 | {91C3BEAC-ACF7-4419-A57D-679912168873} | Water Service Area | L | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 46573 | 184385 | 193126 | 4.74 | 72505.3459272 | 6479.7222905 | 3001.4247113 | 4341.8155148 | 5488.0234146 | 3068.6350750 | 2883.7080242 | 3205.4749351 | 3100.4035000 | 3336.6907659 | 2893.3245566 | 2342.0597477 | 5541.2780373 | 6791.8950710 | 10036.8358151 | 6176.8449416 | 3817.2095264 | 7204.1308118 | 2837.1578291 | 23467.3263083 | 2086.9756387 | 1160.3317865 | 12037.3741485 | 21072.3795945 | 10273.7781043 | 37000.6454705 | 20602 | 17684 | 43047 | 834 | 12464 | 624 | 97872 | 10.67 | 9.16 | 22.29 | 0.43 | 6.45 | 0.32 | 50.68 | 17.49 | 157585.1487857 | 33398.9027882 | 190984.0515740 | MULTIPOLYGON (((-115313.2 7... |
| CALAM - WALNUT GROVE | CA3410047 | {B1588BFA-E492-4087-BF44-72049EB4D5B5} | Water Service Area | P | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 197 | 651 | 12 | -98.16 | 4.6150009 | 0.0316819 | 0.1161671 | 0.1900916 | 0.1795309 | 0.1689703 | 0.0000000 | 0.2640161 | 0.1161671 | 0.0000000 | 0.2956980 | 0.2851374 | 0.1161671 | 1.7741880 | 0.0000000 | 0.7814876 | 0.2956980 | 0.0000000 | 0.0000000 | 1.5840964 | 0.2956980 | 0.2956980 | 0.6336386 | 0.8342908 | 0.3801831 | 2.3972659 | 1 | 0 | 5 | 0 | 0 | 0 | 5 | 5.93 | 0.00 | 44.60 | 0.00 | 3.63 | 0.00 | 45.84 | 15.75 | 10.0537320 | 1.8797944 | 11.9335264 | MULTIPOLYGON (((-132035.3 2... |
| CITY OF SACRAMENTO MAIN | CA3410020 | {8DB287D6-CBB3-484C-802F-FFC909F0959F} | Water Service Area | S | SACRAMENTO | DISTRICT 09 - SACRAMENTO | COMMUNITY | COMMUNITY | 142794 | 510931 | 516189 | 1.03 | 194000.0141638 | 20453.4979307 | 9400.8965823 | 15079.7381061 | 17439.0633155 | 6216.6682096 | 6406.5976684 | 5803.8609561 | 6254.8170189 | 6278.4908829 | 6139.2199311 | 6728.5413058 | 13349.2772832 | 17395.8895949 | 26982.4616746 | 20530.7727300 | 9540.2209738 | 21768.6901173 | 8217.1719086 | 67434.8107773 | 3476.4013912 | 1804.5854572 | 29856.8566750 | 47509.5512539 | 24524.2427584 | 96708.3467115 | 98585 | 62060 | 151211 | 1249 | 34334 | 9242 | 159508 | 19.10 | 12.02 | 29.29 | 0.24 | 6.65 | 1.79 | 30.90 | 15.13 | 431797.3527101 | 77003.0770055 | 508800.4297156 | MULTIPOLYGON (((-128741.8 5... |
Note that there are a number of reasons why the estimated population values are likely to differ from the population numbers in the water system dataset (e.g., the depicted boundaries may not be correct or exact, the supplier may have used different methods to count/estimate the population they serve, the time frames for the estimates may be different, etc.). But, there may also be some cases where the numbers differ significantly – depending on the actual analysis being performed, this may mean that further work is needed for certain areas, or could mean that this method may not be sufficient and different methods are needed.
For water systems with a small population and/or service area, the estimated demographics may not match the population numbers in the original water system dataset very well. You can see this in Table 3 by comparing the POPULATION field, which contains the total population values from the water supplier dataset, with the population_total field, which contains the total population estimated from the census data; the difference between the two is summarized in the population_percent_difference field. This probably indicates that, for small areas, some adjustments and/or further analysis may be needed, and the preliminary estimated values should be treated with some caution/skepticism.
Note: See Section 6.6.2 below for some initial investigation into water systems whose estimated population is zero.
water_systems_sac_demographics %>%
arrange(POPULATION) %>%
slice(1:10) %>%
select(WATER_SY_1, POPULATION, population_total, population_percent_difference) %>%
st_drop_geometry() %>%
kable()| WATER_SY_1 | POPULATION | population_total | population_percent_difference |
|---|---|---|---|
| DELTA CROSSING MHP | 30 | 0 | -100.00 |
| LAGUNA VILLAGE RV PARK | 32 | 20 | -37.50 |
| LINCOLN CHAN-HOME RANCH | 33 | 4 | -87.88 |
| MAGNOLIA MUTUAL WATER | 40 | 1 | -97.50 |
| EDGEWATER MOBILE HOME PARK | 40 | 0 | -100.00 |
| FREEPORT MARINA | 42 | 3 | -92.86 |
| TUNNEL TRAILER PARK | 44 | 0 | -100.00 |
| PLANTATION MOBILE HOME PARK | 44 | 10 | -77.27 |
| SEQUOIA WATER ASSOC | 54 | 0 | -100.00 |
| HAPPY HARBOR (SWS) | 60 | 0 | -100.00 |
But for larger water systems, the estimated population values seem to be more in line with the population numbers in the original dataset. You can see this in Table 4 by, as above, comparing the POPULATION field, which contains the total population values from the water supplier dataset, with the population_total field, which contains the total population estimated from the census data; the difference between the two is summarized in the population_percent_difference field.
water_systems_sac_demographics %>%
arrange(desc(POPULATION)) %>%
slice(1:10) %>%
select(WATER_SY_1, POPULATION, population_total, population_percent_difference) %>%
st_drop_geometry() %>%
kable()| WATER_SY_1 | POPULATION | population_total | population_percent_difference |
|---|---|---|---|
| CITY OF SACRAMENTO MAIN | 510931 | 516189 | 1.03 |
| SACRAMENTO SUBURBAN WATER DISTRICT | 184385 | 193126 | 4.74 |
| SCWA - LAGUNA/VINEYARD | 172666 | 145495 | -15.74 |
| FOLSOM, CITY OF - MAIN | 68122 | 62462 | -8.31 |
| CITRUS HEIGHTS WATER DISTRICT | 65911 | 68912 | 4.55 |
| CALAM - SUBURBAN ROSEMONT | 53563 | 57897 | 8.09 |
| CALAM - PARKWAY | 48738 | 58635 | 20.31 |
| CALAM - LINCOLN OAKS | 47487 | 42916 | -9.63 |
| GOLDEN STATE WATER CO. - CORDOVA | 44928 | 48115 | 7.09 |
| ELK GROVE WATER SERVICE | 42540 | 42647 | 0.25 |
6.4 Plot Results
As a check, we can make some plots to visualize the data before saving.
For plotting, it’ll help to first convert from wide to long format, and adjust the group names so that they can be used as titles.
water_systems_sac_demographics_long <- water_systems_sac_demographics %>%
select(WATER_SY_1, starts_with('percent_')) %>% # select only the fields with percentages, and the water system name/id
# convert to long format
pivot_longer(cols = starts_with('percent_'), names_to = 'metric') %>%
# format the metric field to make titles
mutate(metric = metric %>%
str_remove_all(pattern = 'percent_') %>%
str_replace_all(pattern = '_', replacement = ' ') %>%
str_replace_all(pattern = ' or ', replacement = ' / ') %>%
str_to_title(.) %>%
str_remove_all(pattern = ' / Alaska Native')
)Here’s a view of the structure of the reformatted data:
glimpse(water_systems_sac_demographics_long)Rows: 434
Columns: 4
$ WATER_SY_1 <chr> "HOOD WATER MAINTENCE DIST [SWS]", "HOOD WATER MAINTENCE DI…
$ geometry <MULTIPOLYGON [m]> MULTIPOLYGON (((-132703 403..., MULTIPOLYGON (…
$ metric <chr> "Asian", "Black / African American", "Hispanic / Latino", "…
$ value <dbl> 0.00, 0.00, 69.19, 0.00, 2.10, 0.00, 28.71, 15.91, 24.01, 1…Figure 4 shows each racial/ethic group’s share of the population served by each water system.
plot_results_facet <- ggplot() +
geom_sf(data = water_systems_sac_demographics_long,
aes(fill = value),
color = 'black') +
geom_sf(data = counties_ca %>%
filter(NAME == 'Sacramento'),
color = 'darkgrey',
linewidth = 0.5,
fill = NA) +
facet_wrap( ~ metric, ncol = 4) +
scale_fill_distiller(palette = 'Blues', direction = 1) +
labs(title = 'Community Water Systems in Sacramento County',
fill = '% of Service\nArea Population',
caption = paste0('Data from ', acs_year, ' 5-year ACS')) +
theme_void()
plot_results_facet
Since the values for some groups are hard to distinguish, we can show the results for a single racial/ethnic group.
group_plot <- 'Black / African American'Figure 5 shows the results for the Black / African American racial/ethnic group.
plot_results_1_group <- ggplot() +
geom_sf(data = water_systems_sac_demographics_long %>%
filter(metric == group_plot),
aes(fill = value),
color = 'black') +
scale_fill_distiller(palette = 'Blues',
direction = 1) +
geom_sf(data = counties_ca %>%
filter(NAME == 'Sacramento'),
color = 'darkgrey',
linewidth = 0.5,
fill = NA) +
labs(title = 'Community Water Systems in Sacramento County',
fill = paste0('% ', group_plot, '\nPopulation in Service Area'),
caption = paste0('Data from ', acs_year, ' 5-year ACS')) +
theme_void()
plot_results_1_group
As another example, we may want to look at the portion of all Non-White residents served by water systems, which is shown in Figure 6.
plot_results_non_white <- ggplot() +
geom_sf(data = water_systems_sac_demographics %>%
mutate(percent_non_white = 100 - percent_white), # compute non-white portion of population for each water system
aes(fill = percent_non_white),
color = 'black') +
scale_fill_distiller(palette = 'Blues',
direction = 1) +
geom_sf(data = counties_ca %>%
filter(NAME == 'Sacramento'),
color = 'darkgrey',
linewidth = 0.5,
fill = NA) +
labs(title = 'Community Water Systems in Sacramento County',
fill = '% Non-White Population \nin Service Area',
caption = paste0('Data from ', acs_year, ' 5-year ACS')) +
theme_void()
plot_results_non_white
6.5 Save Results
Once we’ve finished the computations and verified the outputs look reasonable, we can save the results to output files so they can be re-used and shared. The results can be saved in tabular (e.g., csv, excel) and/or spatial (e.g., shapefile, geopackage) formats, which may be helpful for different use cases. Note that you may need to think about exactly what variables to include in the output file(s) and how to format the output datasets (e.g., wide versus long format).
The chunk of code below (which is hidden by default), just tests to see whether any of the datasets to be saved have been changed since the previous version was saved. In general this is probably not needed for a typical workflow and can be ignored for most use cases – it is just used here to make rendering of this document a little more efficient.
Code
# compute hash for datasets to be saved (i.e., a unique identifier for each dataset), and compare against previous versions
## define file that stores hash (unique identifier for dataset)
hash_file <- here('03_data_results',
'dataset_hash.csv')
## compute hashes (unique identifier for datasets)
hash_current <- digest(object = water_systems_sac_demographics,
algo = 'md5')
hash_current_long <- digest(object = water_systems_sac_demographics_long,
algo = 'md5')
hash_table_current <- tibble(dataset = c('water_systems_sac_demographics', 'water_systems_sac_demographics_long'),
hash = c(hash_current, hash_current_long))
## get the previous hashes from file (if it exists), else create a new file to store the hashes
if (file.exists(hash_file)) {
hash_table_previous <- read_csv(file = hash_file)
} else {
file.create(hash_file)
hash_table_previous <- tibble(dataset = c('water_systems_sac_demographics', 'water_systems_sac_demographics_long'),
hash = c('missing', 'missing'))
}
## if new hash is different from previous hash, set flag to update the output file (i.e., write a new version of the file)
file_update <- !identical(hash_table_current %>% filter(dataset == 'water_systems_sac_demographics') %>% pull(hash),
hash_table_previous %>% filter(dataset == 'water_systems_sac_demographics') %>% pull(hash))
file_update_long <- !identical(hash_table_current %>% filter(dataset == 'water_systems_sac_demographics_long') %>% pull(hash),
hash_table_previous %>% filter(dataset == 'water_systems_sac_demographics_long') %>% pull(hash))
## write current hashes to file (for comparison with future versions)
write_csv(x = hash_table_current,
file = hash_file,
append = FALSE)6.5.1 Tabular Dataset
The code below saves the tabular results to a csv file – note that this dataset is in the ‘wide’ format we originally produced the results in:
if (file_update == TRUE) {
write_csv(water_systems_sac_demographics %>%
st_drop_geometry(), # drop the spatial data since this is a tabular format
file = here('03_data_results',
'water_system_demographics_sac.csv'))
}For the tabular data, it might also be useful to save the data in the long/tidy format we developed above when making plots:
if (file_update_long == TRUE) {
write_csv(water_systems_sac_demographics_long %>%
st_drop_geometry(), # drop the spatial data since this is a tabular format
file = here('03_data_results',
'water_system_demographics_sac_long.csv'))
}6.5.2 Spatial Dataset
To save the output in a geospatial format, it may be best to save the data in a wide format, so that all of the attribute data for each target area (water system) is in a single row along with its spatial data (i.e. the system boundary information). The code below saves the results – in wide format – to a geopackage file, which is a spatial file format that is similar to a shapefile.
if (file_update == TRUE) {
st_write(water_systems_sac_demographics,
here('03_data_results',
'water_system_demographics_sac.gpkg'),
append = FALSE)
}6.6 Check – Manual Calculations
To check the interpolation obtained from the aw_interpolate function and demonstrate how it works, we can ‘manually’ perform the same types of weighted average calculation using lower level functions from the sf package. In this case, to keep the operations relatively simple, we’ll just perform the calculation for an individual water system, then check that calculated value against the results obtained from the aw_interpolate function. However, the process is the similar for the other suppliers used in this example.
# pick a system to check
system_check <- 'CARMICHAEL WATER DISTRICT'6.6.1 Total Population
# get the total estimated population for this supplier from above
population_weighted_interp <- water_systems_sac_demographics %>%
filter(WATER_SY_1 == system_check) %>%
pull(population_total)In this case, we’ll check the calculation for the water system: CARMICHAEL WATER DISTRICT. The estimated population for this system using the areal interpolation above (Section 6.2) was: 39,253.
To perform the ‘manual’ calculation, we’ll first get the portions of the census units that intersect the target water system’s service area. They are shown in red in Figure 7 below (the water system boundary is shown in black, and all census units which overlap the system are shown with a randomly chosen fill color).
# get portion of census units that intersect the system area ----
interpolate_manual_pop <- st_intersection(census_data_acs %>%
mutate(total_area = st_area(.)),
water_systems_sac %>%
filter(WATER_SY_1 == system_check))
# make a plot to check the intersection
## plot census units that overlap the system area
plot_manual_check <- ggplot() +
geom_sf(data = census_data_acs %>%
st_filter(water_systems_sac %>%
filter(WATER_SY_1 == system_check)),
aes(fill = GEOID, alpha = 0.8),
# fill = alpha('blue', 0.8),
color = 'grey',
linewidth = 0.5,
show.legend = FALSE) +
scale_fill_manual(values = unname(pal)) +
geom_sf(data = water_systems_sac %>%
filter(WATER_SY_1 == system_check),
fill = NA,
color = alpha('black', 1),
linewidth = 2) +
geom_sf(data = interpolate_manual_pop,
fill = NA,
color = 'red',
linewidth = 0.5) +
theme_void()
# show the plot
plot_manual_check
Then we’ll estimate the population within each of those intersecting portions of the census units by computing an area weighted population. The area weighted population is computed by multiplying the total population of the census unit by the percent of that census unit’s area that overlaps the water supplier’s service area.
interpolate_manual_pop <- interpolate_manual_pop %>%
mutate(intersect_area = st_area(.)) %>%
mutate(weight = drop_units(intersect_area / total_area)) %>%
mutate(population_weighted = population_total * weight)
glimpse(interpolate_manual_pop %>%
select(GEOID, NAME, population_total,
total_area, intersect_area, weight,
population_weighted))Rows: 43
Columns: 8
$ GEOID <chr> "060670058012", "060670079043", "060670058041", "0…
$ NAME <chr> "Block Group 2; Census Tract 58.01; Sacramento Cou…
$ population_total <dbl> 1743, 1103, 1136, 1649, 1750, 2255, 1305, 2030, 12…
$ total_area [m^2] 644333.9 [m^2], 573602.4 [m^2], 742430.6 [m^2], 65…
$ intersect_area [m^2] 497822.3779 [m^2], 436050.8824 [m^2], 17898.9277 […
$ weight <dbl> 0.772615496, 0.760197080, 0.024108554, 0.057156171…
$ population_weighted <dbl> 1346.668809, 838.497379, 27.387318, 94.250527, 175…
$ geometry <GEOMETRY [m]> POLYGON ((-117078 66824.83,..., POLYGON (…The weighted average population for the water system is the sum of the area weighted population within each of the intersecting portions of the census units.
# this should be the same as the interpolated result
population_weighted_manual <- interpolate_manual_pop %>%
pull(population_weighted) %>%
sum() %>%
round(digits = 0)
population_weighted_manual[1] 39253So, as a final check, the ‘manually’ calculated total population for CARMICHAEL WATER DISTRICT is 39,253, and the value obtained from the aw_interpolate function above was 39,253.
6.6.2 Systems with Zero Estimated Population
For some water systems, the estimated population using the areal interpolation above (Section 6.2) was zero, and it may be useful to look at an example to see what’s going on with one of those cases.
# pick a system to check
system_check_zero <- water_systems_sac_demographics %>%
filter(population_total == 0) %>%
arrange(WATER_SY_1) %>%
slice(1) %>%
pull(WATER_SY_1)
# get the total estimated population for this system from above
population_weighted_interp_zero <- water_systems_sac_demographics %>%
filter(WATER_SY_1 == system_check_zero) %>%
pull(population_total)In this case, we’ll check the calculation for the water system: B & W RESORT MARINA. The estimated population for this system using the areal weighted interpolation with the aw_interpolate function above (Section 6.2) was: 0.
To check this result, we’ll first get the portions of the census units that intersect the target water system’s service area, as was done for the other ‘manual’ calculations above. This is shown in Figure 8 below, with the water system boundary is shown in black, census unit(s) overlapping the water system shown in grey, and intersecting portion(s) of the census unit(s) shown in red.
# get portion of census units that intersect the system area ----
interpolate_manual_zero <- st_intersection(census_data_acs %>%
mutate(total_area = st_area(.)),
water_systems_sac %>%
filter(WATER_SY_1 == system_check_zero))
# make a plot to check the intersection
## plot census units that overlap the system area
plot_manual_check_zero <- ggplot() +
geom_sf(data = census_data_acs %>%
st_filter(water_systems_sac %>%
filter(WATER_SY_1 == system_check_zero)),
fill = alpha('grey', 0.8),
color = 'grey',
linewidth = 0.5,
show.legend = FALSE) +
geom_sf(data = water_systems_sac %>%
filter(WATER_SY_1 == system_check_zero),
fill = NA,
color = alpha('black', 1),
linewidth = 2) +
geom_sf(data = interpolate_manual_zero,
fill = NA,
color = 'red',
linewidth = 0.5) +
theme_void()
# show the plot
plot_manual_check_zero
Then we’ll estimate the population within each of those intersecting portions of the census units by computing an area weighted population (as was done above in Section 6.6.1) .
interpolate_manual_zero <- interpolate_manual_zero %>%
mutate(intersect_area = st_area(.)) %>%
mutate(weight = drop_units(intersect_area / total_area)) %>%
mutate(population_weighted = population_total * weight)
glimpse(interpolate_manual_zero %>%
select(GEOID, NAME, population_total,
total_area, intersect_area, weight,
population_weighted))Rows: 1
Columns: 8
$ GEOID <chr> "060670098001"
$ NAME <chr> "Block Group 1; Census Tract 98; Sacramento County…
$ population_total <dbl> 770
$ total_area [m^2] 22675536 [m^2]
$ intersect_area [m^2] 2523.735 [m^2]
$ weight <dbl> 0.0001112977
$ population_weighted <dbl> 0.08569924
$ geometry <POLYGON [m]> POLYGON ((-138282.2 13643.2...The weighted average population for the water system is the sum of the area weighted population within each of the intersecting portions of the census unit(s).
# this should be the same as the interpolated result
population_weighted_manual_zero <- interpolate_manual_zero %>%
pull(population_weighted) %>%
sum()
population_weighted_manual_zero[1] 0.08569924In this case, the (un-rounded) ‘manually’ calculated total population for B & W RESORT MARINA is 0.08569924 (for reference, the rounded value obtained from the aw_interpolate function was 0). From the Figure 8 above, you can see that the service area reported for this system is very small, only covering a small fraction of a single census unit, resulting in a population estimate that is very low. In this case, it could be that the system area was drawn incorrectly (i.e., maybe it doesn’t really depict the entire service area), in which case the reported service area should be revised. Or, it’s possible that the population within the given census unit is very un-evenly distributed and instead there’s a relatively high density population cluster in the depicted service area, in which case a more sophisticated method than an area-weighted average should be used (e.g., maybe consider the density of buildings, roads, and/or other features associated with inhabited areas).
7 Alternative Computation Methods
Warning
This section is in progress…
7.1 Population Weighted Interpolation
Warning
This section is in progress…
This could be used for estimating certain types of metrics (e.g., median household income, CalEnviroScreen scores), using known or estimated population distributions (i.e., not used directly to determine population count in a given area).
There may be a few ways to do this:
- ‘Manual’ calculation – e.g., estimate population of overlapping census units (using total population multiplied by % of the census unit within the target area), then use those derived populations as weights (rather than the area of overlap)
- Use the
interpolate_pwfunction from thetidycensuspackage (I think this is based on the methodology described here but need to investigate further).
As a rough example of option #2:
First get 2020 census population by census block (not strictly necessary, but this is the most spatially granular census population data available):
# get census data
census_data_blocks <- get_decennial(geography = 'block', # can be 'block', 'block group', 'tract', 'county', etc.
state = 'CA',
county = counties_list,
year = 2020,
variables = c(
'population_total' = 'P2_001N'
),
output = 'wide', # can be 'wide' or 'tidy'
geometry = TRUE,
cache_table = TRUE) Getting data from the 2020 decennial CensusUsing the PL 94-171 Redistricting Data Summary FileThen interpolate:
results_interp_popwt <- interpolate_pw(from = census_data_filter %>%
st_transform(crs_projected) %>%
filter(!is.na(median_household_income)) %>%
select(median_household_income),
to = water_systems_sac %>%
st_transform(crs_projected),
to_id = 'WATER_SY_1',
extensive = FALSE,
weights = census_data_blocks %>%
st_transform(crs_projected),
# weight_placement = 'surface',
weight_column = 'population_total') %>%
rename(median_household_income_popwt = median_household_income) # rename results fieldresults_interp_popwt <- results_interp_popwt %>%
left_join(water_systems_sac %>%
st_drop_geometry() %>%
select(SERVICE_CO, POPULATION, WATER_SY_1),
by = 'WATER_SY_1')
glimpse(results_interp_popwt)Rows: 62
Columns: 5
$ WATER_SY_1 <chr> "HOOD WATER MAINTENCE DIST [SWS]", "MC C…
$ median_household_income_popwt <dbl> 56250.00, 60521.00, 68248.00, NA, 29468.…
$ SERVICE_CO <dbl> 82, 199, 34, 64, 128, 83, 28, 50, 164, 5…
$ POPULATION <dbl> 100, 700, 40, 150, 256, 150, 32, 100, 35…
$ geometry <MULTIPOLYGON [m]> MULTIPOLYGON (((-132703 403…Note that not all water systems get an estimated value using this method (even if NA values are removed from the source data) – a total of 17 systems are missing results with this method, which tend to be the smallest water systems.
8 Working with Other (Non-Census) Source Data
Warning
This section is in progress…
8.1 CalEnviroScreen
Warning
This section is in progress…
References
Parry, Josiah. 2023. “Arcgislayers: An r Interface for ArcGIS REST Services.”
Pebesma, Edzer, and Roger Bivand. 2023. “Spatial Data Science: With Applications in r.” https://doi.org/10.1201/9780429459016.
Prener, Christopher, Revord, and Charles. 2019. “areal: An R package for areal weighted interpolation.” Journal of Open Source Software 4 (37). https://doi.org/10.21105/joss.01221.
R Core Team. 2023. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.
Walker, Kyle. 2023a. “Tigris: Load Census TIGER/Line Shapefiles.” https://CRAN.R-project.org/package=tigris.
———. 2023b. “Analyzing US Census Data,” January. https://doi.org/10.1201/9780203711415.
Walker, Kyle, and Matt Herman. 2023. “Tidycensus: Load US Census Boundary and Attribute Data as ’Tidyverse’ and ’Sf’-Ready Data Frames.” https://CRAN.R-project.org/package=tidycensus.
Wickham, Hadley, Mara Averick, Jennifer Bryan, Winston Chang, Lucy D’Agostino McGowan, Romain François, Garrett Grolemund, et al. 2019. “Welcome to the Tidyverse” 4: 1686. https://doi.org/10.21105/joss.01686.